The Role of Monetary Policy in Turkey
during the Global Financial Crisis
Harun Alp and Selim Elekdag
WP/11/150
© 2011 International Monetary Fund WP/11/150
IMF Working Paper
Asia and Pacific Department
The Role of Monetary Policy in Turkey during the Global Financial Crisis
Prepared by Harun Alp and Selim Elekdag
1
Authorized for distribution by Roberto Cardarelli
June 2011
Abstract
Turkey is an interesting case study because it was one of the hardest hit emerging
economies by the global financial crisis, with a year-over-year contraction of 15 percent
during the first quarter of 2009. At the same time, anticipating the fallout from the crisis,
the Central Bank of the Republic of Turkey (CBRT) decreased policy rates by an
astounding 1025 basis points over the November 2008 to November 2009 period. In this
context, this paper addresses the following broad question: If an inflation targeting
framework underpinned by a flexible exchange rate regime was not adopted, how much
deeper would the recent recession have been? Counterfactual experiments based on an
estimated structural model provide quantitative evidence which suggests that the recession
would have been substantially more severe. In other words, the interest rate cuts
implemented by the CBRT and exchange rate flexibility both helped substantially soften
the impact of the global financial crisis.
JEL Classification Numbers:E5, F3, F4, C11
Keywords: financial accelerator, Bayesian estimation, DSGE models, financial crises, sudden
stops, monetary policy, Turkey, emerging economies, emerging markets
Author‘s E-Mail Address: [email protected]; [email protected]
1
Harun Alp is with the Central Bank of the Republic of Turkey (CBRT). The views expressed in this paper belong solely to
the authors and should not be affiliated with any institution the authors are or have been affiliated with. We thank Anella
Munro, Zahid Samancıoğlu, Ümit Özlale, Fatih Özatay, Hakan Kara, Özer Karagedikli, Güneş Kamber, Cecilia Lon and
Refet Gürkaynak as well as conference participants at the Turkish Economic Association, CBRT, Reserve Bank of New
Zealand, Koç University and the TOBB University of Economics and Technology for their insightful comments.
This Working Paper should not be reported as representing the views of the IMF.
The views expressed in this Working Paper are those of the author(s) and do not
necessarily represent those of the IMF or IMF policy. Working Papers describe research
in progress by the author(s) and are published to elicit comments and to further debate.
2
Contents Page
Executive Summary ...................................................................................................................4
I. Introduction .......................................................................................................................5
II. Economic Developments in Turkey: The Role of Macroeconomic Reforms ...................7
III. The Model .........................................................................................................................9
IV. Estimation ........................................................................................................................12
V. Model Dynamics .............................................................................................................13
VI. Historical Decompositions ..............................................................................................15
VII. The Role of Monetary Policy during the Crisis ...............................................................17
VIII. Summary and Main Policy Implications .........................................................................21
References ................................................................................................................................71
Figures
1. Turkey: Selected Macroeconomic Indicators ....................................................................52
2. Model Schematic ...............................................................................................................53
3. Model Predictions versus the Data ....................................................................................54
4. Dynamic Responses to a Monetary Policy Shock .............................................................55
5. Turkey: The Monetary Transmission Mechanism .............................................................56
6. Historical Decomposition: The Role of Monetary Policy .................................................57
7. Historical Decomposition: Crisis Shocks ..........................................................................58
8. Historical Decomposition: Other Supply and Demand Shocks .........................................59
9. Counterfactual Scenarios: The Role of Monetary Policy and Real GDP ..........................60
Tables
1. Turkey: Financial Ratios across Selected Industries .........................................................46
2. Calibrated Parameters ........................................................................................................47
3. Prior and Posterior Distributions .......................................................................................48
4. Sensitivity Analysis ...........................................................................................................49
5. The Role of Monetary Policy and Financial Reforms .......................................................50
6. Measuring the Severity of Economic Contractions ...........................................................51
Appendix ..................................................................................................................................23
Appendix Figures
1a. Prior Posterior Distributions (Parameters) .........................................................................62
3
1b. Prior Posterior Distributions (Standard Deviations of Shocks) .........................................63
1c. Prior Posterior Distributions (Shock Processes Parameter) ...............................................64
2a. Impulse Responses: UIP Shock .........................................................................................65
2b. Impulse Responses: Foreign Interest Rate Shock ..............................................................65
3a. Impulse Responses: Foreign Demand Shock .....................................................................66
3b. Impulse Responses: Net Worth Shock ...............................................................................66
4a. Impulse Responses: Unit Root Technology Shock ............................................................67
4b. Impulse Responses: Stationary Technology Shock ...........................................................67
5a. Impulse Responses: Domestic Mark-up Shock .................................................................68
5b. Impulse Responses: Foreign Inflation Shock ....................................................................68
6a. Impulse Responses: Government Spending Shock ............................................................69
6b. Impulse Responses: Preference Shock...............................................................................69
7. Counterfactual Scenarios: Monetary Policy and Growth ..................................................70
Appendix Table
1. Alternative Measuring of Actual and Simulated Recessions .............................................61
4
EXECUTIVE SUMMARY
This paper argues that the monetary policy implemented by the Central Bank of the Republic
of Turkey (CBRT) helped soften the impact of the global financial crisis. Specifically, the
findings suggest that without key reforms—including the adoption of an inflation targeting
framework underpinned by a flexible exchange rate regime—the global financial crisis
would have been associated with a much deeper economic contraction.
Turkey is an interest emerging economy case study because it was one of the hardest hit
countries by the crisis, with a year-over-year contraction of 14.7 percent during the first
quarter of 2009. At the same time, anticipating the fallout from the crisis, the CBRT
decreased policy rates by an astounding 1025 basis points over the November 2008 to
November 2009 period.
Against this backdrop, the general question this paper attempts to address is the following:
Did the monetary policy implemented by the CBRT help soften the impact of the recent
crisis? In terms of monetary policy, we focus on the role of being able to implement
countercyclical and discretionary monetary policy (through changes in the short-term interest
rate) within an inflation targeting regime consistent with exchange rate flexibility. In this
context, we seek a quantitative answer to the following question:
If an inflation targeting framework underpinned by a flexible exchange rate regime had
not been adopted, how much deeper would the recent recession have been?
This paper finds that the recession would have been substantially more severe.
The most intuitive way to communicate our quantitative results is by taking the growth rate
during the most intense year of the global financial crisis, namely 2009, as our baseline.
Model-based counterfactual simulations indicate that without the countercyclical and
discretionary interest rates cuts implemented by the CBRT, growth in 2009 would have
decreased from the actual realization of –4.8 percent to –6.2 percent. Moreover, if a fixed
exchange rate regime would have been in place instead of the current inflation targeting
regime (which is underpinned by a flexible exchange rate), the results indicate that growth in
2009 would have been –8.0 percent, a difference from the actual outcome of 3.2 percentage
points. In other words, these simulations underscore the favorable output stabilization
properties owing to the combination of countercyclical monetary policy and exchange rate
flexibility.
These finding are based on counterfactual simulations derived from an estimated dynamic
stochastic general equilibrium (DSGE) model which, along with standard nominal and real
rigidities, includes a financial accelerator mechanism in an open-economy framework.
In sum, without the adoption of an inflation targeting framework underpinned by a flexible
exchange rate regime, the impact of the recent global financial crisis would have been
substantially more severe.
5
I. INTRODUCTION
Distinct features of the global financial crisis which intensified during September
2008 include a sharp slowdown in global economy activity—including severe recessions
across many countries—along with an episode of acute financial distress across international
capital markets. Another departure from past global downturns was the coordination of
unprecedented countercyclical policy responses to the crisis, which seems to have supported
the rebound in economic activity.
Turkey was one of the hardest hit countries by the crisis. Real GDP contracted sharply for
four quarters, reaching a year-over-year contraction of 14.7 percent during the first quarter of
2009, resulting in a –4.8 percent annual growth rate for that year. At the same time,
anticipating the fallout from the crisis, the Central Bank of the Republic of Turkey (CBRT)
decreased policy rates by an astounding 1025 basis points over the November 2008 to
November 2009 period.
The recent Turkish experience differs from the past in several dimensions. As discussed
further in Section II, Turkey suffered from an intense financial crisis in 2001. While the
2001 crisis was certainly harsh, it was followed by at least two important reforms. First, the
pegs and heavily managed exchange rate regimes of the past were replaced by a flexible
exchange rate regime. Second, and relatedly, the policy framework of the CBRT gradually
transitioned into a full-fledged inflation targeting regime.
Against this backdrop, this paper will focus on the macroeconomic implications of these two
monetary policy reforms, particularly during the recent global financial crisis. The principle
question of the paper is as follows: What was the role of these changes to the monetary
policy framework in mitigating the severity of the recent recession? More specifically, we
seek to address the following set of questions: (1) In contrast to the fixed exchange rate
regimes of the past, what was the role of exchange rate flexibility in helping insulate the
economy from the crisis? (2) Relatedly, consistent with the attainment of the inflation targets,
what was the role of the CBRT‘s countercyclical interest rate cuts in softening the impact of
the crisis?
This paper seeks to provide quantitative answers to these questions. To this end, we develop
and estimate a small open economy dynamic stochastic general equilibrium (DSGE) model
designed to capture salient features of the Turkish economy. The model contains a number of
nominal and real frictions such as sticky prices, sticky wages, variable capital utilization,
investment adjustment costs, habit persistence, and incorporates a financial accelerator
mechanism à la Bernanke and others (1999) in an open-economy setup to better fit the data.
Details regarding the setup of the model, the estimation procedure, its robustness, and its
dynamics are briefly covered in Section III through Section V (with many of the details
relegated to an extensive appendix).
6
Using the estimated structural model we can address the main question of the paper
reformulated as follows:
If an inflation targeting framework underpinned by a flexible exchange rate regime
was not adopted, how much deeper would the recent recession have been?
This paper finds that the recession would have been substantially more severe.
We derive this result using model-based counterfactual simulations. These simulations
represent the basis for our main policy implications and are discussed in detail in Section VI
and Section VII. We contrast the actual realization of real GDP (the baseline scenario), with
other counterfactual scenarios that, for example, consider the how the economy would have
responded if the CBRT had not implemented any discretionary monetary policy loosening.
To more intuitively convey our quantitative results, we consider the growth rate during the
most intense year of the global financial crisis, namely 2009, as our baseline. In this context,
our counterfactual simulations indicate that without the discretionary interest rate cuts
(expansionary monetary policy shocks) possible under the inflation targeting regime, growth
in 2009 would have decreased from the actual realization of –4.8 percent to –5.9 percent, a
difference of 1.1 percentage point. This lies within the range found by Christiano and others
(2008), which finds growth contributions of monetary policy of 0.75 percent and 1.27 percent
for the United States and the Euro area, respectively.
Other insightful counterfactual experiments are possible. For example, if there was
absolutely no countercyclical responses to the crisis—in other words the CBRT did not take
the output gap into account and at the same time did not implement any discretionary policy
loosening (no expansionary monetary policy shocks)—then the 2009 growth outcome would
have been –6.2 percent. Moreover, if a fixed exchange rate regime would have been in place
instead of the current inflation targeting regime which operates with a flexible exchange rate,
the results indicate that growth in 2009 would have been –8.0 percent, a difference from the
actual outcome of 3.2 percentage points.
In sum, without the adoption of the flexible exchange rate regime, and active countercyclical
monetary policy guided by an inflation targeting framework, the impact of the recent global
financial crisis would have been substantially more severe. As emphasized in the final
section of the paper, the inflation targeting framework underpinned by a flexible exchange
rate seems to have increased the resilience of the Turkish economy to shocks. The inflation
targeting framework allowed the CBRT to implement countercyclical and discretionary
interest rate cuts, while exchange rate flexibility acted as a shock absorber, both of which
increased the resiliency of the economy. The latter result echoes the favorable output
stabilization properties of exchange rate flexibility which can be traced back to at least to the
seminal contributions of Mundell and Fleming.
7
Our paper builds on a tradition of small open economy DSGE models popularized by
Mendoza (1991). Over time, these real models were augmented with nominal rigidities to
motivate and then explore the implications of monetary policy (for example, Gali and
Monacelli, 2002, among others). To capture financial frictions more appropriately, building
on Bernanke and others (1999), a financial accelerator mechanism was also added on to these
models (see for example, Cespedes and others, 2004; Devereux, and others, 2006; Gertler,
and others, 2007; as well as Elekdag and Tchakarov, 2007).
With the growing feasibility and popularity of Bayesian method, building upon the closed
economy studies of Smets and Wouters (2003, 2007), small open economy models were
estimated (Lubik and Schorfheide, 2007; Teo, 2006; as well as Christensen and Dib, 2006).
Then, Elekdag, Justiniano, and Tchakarov (2006) estimated a small open economy model
with a financial accelerator for an emerging market, which later motivated others do follow
suit using richer modeling structures (see, for example, Garcia-Cicco, 2010). Against this
backdrop, this paper takes Elekdag, Justiniano, and Tchakarov (2006) as a starting point, and
augments their model with some of the features in Gertler and others (2007), Smets and
Wouters (2007) to improve model fit and to facilitate the counterfactual simulations
discussed below.
II. ECONOMIC DEVELOPMENTS IN TURKEY: THE ROLE OF MACROECONOMIC REFORMS
By way of background for the rest of the paper, the main objective of this section is to briefly
discuss some key developments regarding the Turkish economy over the last two decades.
2
In particular, we would like to focus on a few key macroeconomic policy reforms that we
argue helped soften the impact of the global financial crisis which intensified after the
Lehman Brothers bankruptcy.
It will be useful to draw attention to the macroeconomic turbulence in Turkey during the
1990s (which included a financial crisis in 1994) as reflected in some selected
macroeconomic indicators shown in Figure 1. How does the recent Turkish experience differ
from the past? To address this question, we take the intense financial crisis of 2001 as our
point of departure, which was associated with fragilities in the banking system and a
speculative attack on the fixed-exchange rate regime in place at the time. A severe recession
ensued.
After the 2001 crisis, Turkey embarked on a new IMF-supported arrangement. For the
purposes of this paper, two major reforms that were implemented in the aftermath of the
crisis are emphasized:
2
For more a comprehensive perspective on crises in Turkey, see Özatay (2009) which is in Turkish, or Yalçın
and Thomas (2010) which focuses on the most recent crisis and is in English.
8
First, the heavily managed and fixed exchange rates regimes of the past were abandoned
in favor of floating exchange rates.
Second, and relatedly, the CBRT started its transition, and in 2006, officially
implemented a full-fledged inflation targeting regime which would serve as the
economy‘s nominal anchor.
Over the next 26 quarters, from the first quarter of 2002 to mid-2008, the Turkish economy
grew by over five percent (year-over-year), and inflation declined markedly.
3
While global
economic and financial conditions were favorable, it is hard to argue that the reforms
mentioned above did not contribute positively toward achieving these growth rates.
4
With the intensification of the global financial crisis during the fall of 2008, synchronized
downturns coupled with financial stress affected international capital markets and economies
across the world. As expected, the Turkish economy was severely affected by this abrupt
collapse of the global economy. In fact, the contraction in world demand hit Turkish exports
with severe implications for the rest of the economy. At the same time, the shock to global
financial markets resulted in a collapse of asset prices (including the currency), an increase in
spreads, and sizeable capital outflows. In addition, the heightened uncertainty associated with
the unprecedented nature of this global financial crisis reinforced the foreign demand and
financial shocks as well as acted as another channel suppressing consumption, investment,
and credit extension. Therefore, for the purposes of this paper, we argue that the Turkish
economy was unfavorably affected by a collapse in foreign demand, distress across
international capital markets, and heightened uncertainty.
As a result, Turkey was one of the hardest hit countries by the crisis. Real GDP contracted
sharply for four quarters, reaching a year-over-year contraction of 14.7 percent during the
first quarter of 2009, resulting in a –4.8 percent annual contraction. The CBRT grasped the
implications of this dire situation relatively early on. Anticipating substantially reduced
levels of resource utilization, and in an attempt to mitigate the impact of the crisis on the
economy, the CBRT cut interest rates by an astounding 1025 basis points over the November
2008 to November 2009 period. But to what end? We seek to address this question below.
3
It is also useful to indicate that the Turkish banking system was nearly completely overhauled after the 2001
crisis. Excessive leverage, maturity and currency mismatches which aggravated the severity of the 2001 crisis
declined markedly. Evidence suggests that this lower risk profile became widespread as shown in the lower
leverage ratios shown in Table 1. While the financial, insurance, and real estate sectors are shown together with
public administration, it is well known that the risk management practices across the banking system improved.
This is important because a sounder financial system increases the effectiveness of the monetary transmission
mechanism.
4
In fact, the resiliency of the economy was vindicated after successfully coping with the turbulence during mid-
summer of 2006 caused by a sell-off of assets across many emerging economies.
9
III. THE MODEL
This section presents an overview of the structural model underpinning our quantitative
results. As mentioned above, readers primarily interested in the main policy implications of
the paper could directly proceed to Section VII and Section VIII. The goal here is to present
the general intuition of the model, while the details are relegated to the Appendix. The
structural framework builds upon a core (New) Keynesian model. The model used is an
open-economy variant of what the literature refers to as a New Keynesian dynamic stochastic
general equilibrium (DSGE) model. However, to better fit the data, the model is augmented
with a number of features including real and nominal rigidities (including, for example,
investment adjustment costs and sticky wages), as well as a financial accelerator mechanism
(to capture financial market imperfections) among several others.
5
The model consists of several agents including households, producers, and the government.
There are three types of producers: entrepreneurs, capital producers, and retailers. The
government is responsible for implement monetary and fiscal policy. A visual representation
of the flow of goods and services across these agents is shown in Figure 2. However, rather
than elaborate on all aspects of the model, this goal in this section is to focus on the
transmission of certain shocks and the role of monetary (and exchange rate) policy.
The transmission of shocks
For the purposes of this paper, we argue that during the global financial crisis, the Turkish
economy was unfavorably affected by a collapse in foreign demand, distress across
international capital markets, and heightened uncertainty. To assess this assertion, we posit
that in terms of our model, these disturbances are captured by an export demand shock, a
sudden stop shock, and a (financial) uncertainty shock. We now review each of these in turn.
Later, we actually provide quantitative evidence that appraises the relative growth
contribution of these three shocks (as well as the other structural shocks) during the recession
which intensified in the first quarter of 2009.
The export demand shock
The export demand shock, or perhaps equivalently, the foreign demand shocks propagates
through the model via the market clearing condition below:
5
In terms of theory, our model brings together elements from papers including Adolfson and others (2007),
Bernanke and others (1999), Elekdag and Tchakarov (2007), and Gertler and others (2007) among many others,
while, in order to facilitate estimation, we build on the work of Smets and Wouters (2003, 2007) and Elekdag
and others (2006).
10
Leaving aside differences in notation, this is basically the standard aggregate demand identity
for home (domestically produced) goods, which posits that domestic output is equal to the
sum of consumption of domestically produced goods (which is the sum of both household
and entrepreneurial consumption,
), domestic investment goods,
, government
expenditures,
, and exports,
. Therefore, leaving the other details to the complete model
description in the Appendix (which also describes import demand), a collapse in export
(foreign) demand is simply represented by a decline in
The sudden stop shock
Turkey‘s experience during the global financial crisis was also associated with a reversal of
capital inflows (a sudden stop in the parlance of Calvo and others, 2004), as well as a sharp
depreciation of the exchange rate. To capture these interrelated disruptions, we augment the
uncovered interest parity (UIP) condition with a shock as in many other papers as follows:
where
and
, represent the domestic and international (gross) interest rates, respectively,
denotes the nominal exchange rate (Turkish lira per U.S. dollar—an increase represents a
depreciation),
is the expectations operator (conditional on information up to time t), and
is the sudden stop shock (also referred to an exchange rate shock, UIP shocks, and some
other in the literature). Therefore, as in Gerlter and others (2007), a shock that triggers large
capital outflows is captured by this exogenous terms which is appended to an otherwise
standard UIP condition. This sudden stop shock would serves to capture an important
dimension of the financial aspect of the recent crisis.
The (financial) uncertainty shock
The description of this shock warrants some background. In this model, the real cost of
capital departs from the standard representation in other studies because of the existence of
an external finance premium. Consider the equation below:
where we have that the real cost of capital,
, is equal to the real interest rate,
,
augmented by the external finance premium represented by the term
. In turn, the
external finance premium depends on the leverage ratio (assets scaled by net worth) of the
entrepreneurs:
11
Note that total assets,
, depends on the price of equity,
, which is not sticky (by
contrast to goods prices or wages). This implies that the leverage ratio is quite sensitive to
asset price fluctuations.
The precise specification of the evolution of net worth,
, is complex (and shown in the
Appendix), so here we use an abridged version:
where
and
denote the entrepreneurial wage bill and the value of the firm, respectively.
The (financial) uncertainty shock is an exogenous process, represented by the term,
, which
by construction has direct impact on the level of aggregate net worth and therefore the
external financial premium. Put differently, the net worth shock could be interpreted as a
shock to the rate of destruction of entrepreneurial financial wealth (in line with several other
studies). This shock directly affects entrepreneurial net worth and has been used in various
forms by Elekdag and others (2006), Curdia (2007), Christiano and others (2010), and more
recently by Ozkan and Unsal (2010). Another way to think about this shock is that it could be
thought of capturing counterparty risk—owing part to Knightian uncertainty—a key
consideration during the global financial crisis. This heightened uncertainty regarding cash
flows, for example, would impair assets and thus disrupt the financial system.
What role for monetary policy?
In our model, the central bank alters interest rates in an attempt to achieve certain policy
objectives. Before proceeding to the details, note that the policy rule to be described below
implies that the monetary authority sets the nominal interest rate, taking into consideration
the inflation rate deviation from the time-varying inflation target, the output gap, the rate of
exchange rate depreciation, and the previous period‘s interest rate (policy smoothing).
A simplified version of the interest rate rule takes the following (log-linear) form (see
Appendix for further details):
where, in this flexible specification,
,
,
,
denote the (short-term policy) interest
rate, the (core CPI) inflation rate, the output gap, and the nominal exchange rate,
respectively. Note that
denotes the monetary policy shock—interest rate changes that
deviate from the (empirical) interest rate rule would be captured by this disturbances and
could be considered discretionary monetary policy. The time-varying inflation target,
, is
assumed to evolve according to the following stochastic process:
12
The time-varying inflation target captures the reality that the inflation target in Turkey was
changed over time. However, it has also been used in the literature to capture structural
changes in the conduct of monetary policy that are not captured otherwise (see Adolfson and
others, 2007, for further details).
Anticipating the results to follow, notice that when the output gap is negative—that is, output
is below potential—strict adherence to the rule above would imply that the interest rate
decreases by an amount dictated by the coefficient
. However, the monetary authority
might decrease interest rates by more than what the systematic component of the rule would
imply. Recall that this deviation from the rule is capture by the error term,
, which is the
monetary policy shock—thereby capturing discretionary monetary loosening. As will be
discussed in further detail below, during the most intense episode of the global financial
crisis, interest rates decreased by more than the amount the empirical counterpart of the rule
would have implied, helping soften the impact of the global financial crisis.
IV. ESTIMATION
This section gives an overview of model estimation. It briefly reviews issues pertaining to
data, parameter calibration, the choice of prior distributions, the resulting posterior
distributions, model fit, and sensitivity analysis. An extensive discussion of these issues is
covered in the Appendix.
Data
The log-linearized model is estimated using Bayesian methods primarily developed by
Schorfheide (2000), and later popularized by Smets and Wouters (2003, 2007). The model is
estimated using quarterly data from the first quarter of 2002 to the second quarter of 2010
using the series shown in Figure 3. In line with many other studies, we have chosen to match
the following set of twelve variables:
the levels of the domestic policy and foreign interest
rates, the inflation rates of domestic GDP deflator and core consumer price and foreign
consumer price indices, as well as the growth rates of GDP, consumption, investment,
exports, imports, foreign GDP, and the real exchange rate. The sample period used for
estimation covering the 2002–2010 period under consideration captures the episode when the
CBRT was transitioned to an inflation targeting regime (initially implicitly, and the explicitly
starting in 2006).
Model Parameters
We followed the literature and calibrate certain parameters (see, for example, Christiano and
others, 2010), which could be thought of as infinitely strict priors. Many of the parameters
are chosen to pin down key steady state ratios, while the remaining parameters are taken
from the literature as summarized in Table 2.
13
The remaining 43 parameters, shown in Table 3, are estimated. These parameters determine
the degree of the real and nominal rigidities, the monetary policy stance, as well as the
persistence and volatility of the exogenous shocks. The table shows the assumptions
pertaining to the choice of distribution, the means, standard deviations, or degrees of
freedom. The choice of priors is in line with the literature.
The posterior estimates of the variables are also shown in Table 3. The table reports the
means along with the 5
th
and 95
th
percentiles of the posterior distribution of the estimated
parameters obtained through the Metropolis-Hastings sampling algorithm. In general, the
parameter estimates are in line with those found in other studies.
An initial assessment of model fit and sensitivity analysis
In terms of assessing the fit of the model, we start off by comparing the data with the
baseline model‘s one-sided Kalman filter estimates of the observed variables, and then
consider model robustness in the following section. The data and the filtered variables are
shown in Figure 3 indicating that the sample fit is generally quite satisfactory.
To assess the robustness of the estimated model, we consider a battery of alternative
specifications which include different monetary policy rules and alternative structural
features. The results are summarized in Table 4, which depicts the log data density of the
various models, and the posterior odd ratio contrasting the baseline and the alternative model
specifications. While the details are discussed extensively in the Appendix, the main
takeaway is that we consider 18 alternative specifications, and the results are very strongly, if
not decisively, in favor of the baseline.
V. MODEL DYNAMICS
This section aims to explore the dynamics of the estimated model. It starts off by exploring
the implications of a monetary policy shock, and then provides an overview of the dynamics
associated with the other shocks relegating the details to the Appendix.
The monetary transmission mechanism
We start off by considering the monetary transmission mechanism in Turkey. This is critical
because the focus of the paper is to assess the role of monetary policy during the global
financial crisis.
To this end, we consider the impulse responses to a one standard deviation monetary
tightening shock as shown in Figure 4. Also note that we compare models with and without
the financial accelerator, to assess how financial frictions affect the monetary transmission
mechanism. The shock propagation is effected via three main channels:
14
The first channel operates as interest rates affect domestic demand, which primarily
comprises of consumption and investment. Working through the Euler equation,
higher real interest rates foster an increase in saving as consumption is postponed to
later periods. At the same time, higher real interest rates increase the opportunity cost
of investment, decreasing the rate of capital accumulation (a channel that is
operational in models with capital). As a result, domestic demand and output
decreases, putting downward pressure on inflation.
The second channel brings out the open economy features of the model as it works
via the exchange rate. Because of the nominal rigidities, the increase in the nominal
interest rate translates into higher real interest rates and is associated with an increase
in the real exchange rate. In turn, this appreciation of the real exchange rate
suppresses net exports (the expenditure switching effect), further decreasing
aggregate demand.
The third channel is characterized by the financial accelerator mechanism. Higher
interest rates depress asset prices (the real price of capital) bringing about a
deterioration in net worth. Weaker balance sheet fundamentals cause an increase in
the external finance premium thereby raising the opportunity cost of investment
above and beyond the initial effect generated by the monetary tightening. As
indicated in Figure 4, this brings about an even sharper contraction in investment,
which is the primary determinant of the deeper contraction. As is clear in the impulse
responses, the financial accelerator mechanism can amplify the effects of certain
shocks (as discussed in Bernanke, Gertler, and Gilchrist, 1999) and is further
explored in the Appendix.
To more openly communicate the degree of uncertainty regarding the monetary transmission
mechanism in Turkey during a sample period which encompasses the global financial crisis,
Figure 5 presents Bayesian impulses response functions for a selected set of variables along
with their 90 percent bands which take into consideration parameter uncertainty. As shown in
the Table 3, a one standard deviation contractionary monetary policy shock corresponds to a
70 basis point (quarterly) increase in the nominal interest rate—in other words, an annual
increase in the policy rate of about three percent. The impulse response functions indicate
that the output gaps dips below the steady state by 70 basis points, whereas the year-over-
year inflation rate reaches a trough of about 140 basis points below steady state after four
periods.
6
6
A shock to the time-varying inflation target is also represents a change in monetary policy (Smets and
Wouters, 2003). As will be discussed in detail below, this shock barely affects output, we opted not to focus on
it here. Suffice to say, that the impulses responses are broadly similar to those shown in Adolfson and others
(2005), with differences owing to the fact that they calibrate the persistence coefficient to 0.975, whereas we
find an estimated value of 0.77.
15
The model includes 15 structural shocks including the monetary policy shock discussed
above. For the purposes of this paper, a detailed discussion of the impulse responses of the
remaining shocks is relegated to the Appendix in order to proceed to the sections of the paper
which presents our main results and policy implications.
VI. HISTORICAL DECOMPOSITIONS
This section seeks to better understand the contributions of the structural shocks to output
growth. Of course, in line with the main theme of the paper, the key structural shock we will
focus on is the monetary policy shock. In this context, the section will quantify the role of
monetary policy shocks on output growth, and will therefore provide one of our main policy
implications.
For the purposes of this paper, we categorize the 15 structural shocks in the model into three
groups to reinforce intuition. The first group consists of the monetary policy shocks and is
the focus of this section. The second group comprises the crisis shocks, namely, shocks to
foreign demand, financial uncertainty, and the uncovered interest rate parity (the sudden stop
shock), and the final group contains the remaining supply and demand shocks. Our goal here
is to assess the role of these groups of shocks on (year-over-year) output growth over the
2005–2010 period, which includes the run-up and the most intense episode of the global
financial crisis.
What was the growth contribution of the monetary policy shocks?
The main takeaway of this section is shown in Figure 6. The figure plots real (year-over-
year, demeaned) GDP growth, as well as the growth contributions of the three groups of
shocks described above. The figure addresses the following question: What was the growth
contribution of the monetary policy shocks? The monetary policy shocks are shown in black,
and as is clear from the figure, they positively contributed to output growth during the crisis
episode.
As we discuss in extensive detail in the next section, the average growth contribution of
the monetary policy shocks during the crisis episode is about 1.1 percent. To put this
number in perspective, recall that the year-over-year real GDP contraction in Turkey in
2009 was –4.8 percent. Without these monetary policy shocks, that is discretionary
departures from the estimated interest rate rule, our model indicates that the growth rate for
this year would have been –5.9 percent instead. In other words, monetary policy seems to
have markedly contributed the softening the impact of the global financial crisis. We contrast
this growth contribution of 1.1 percent to those in the literature in the following section
below.
16
What was the role of the other structural shocks?
Consider first the role of the crisis shocks. To better understand the effects of the second
group of shocks (foreign demand, risk premium, and financial uncertainty), each of these
shocks is shown separately along with real (demeaned, year-over-year) GDP growth in
Figure 7. To start off, however, note that the sudden stop (UIP or risk premium) shock does
not seem to have an important effect on growth during the crisis. A key reason could be that
in contrast to Cespedes, Chang, and Velasco (2004) as well as Elekdag and Tchakarov (2007)
we follow the initial specification of Gertler and others (2007) and posit that entrepreneurs
borrow in domestic- rather the foreign-currency denominated debt. This arguably could
reduce the role of risk premium (UIP) shocks, an important determinant of exchange rate
dynamics. However, given that foreign currency exposure in Turkey has generally decreased
markedly after 2002, and because it was never as serious an issue as in some Latin American
countries, for example, we do not pursue this (straightforward) extension in this paper, but
leave it for future research.
The role of the crisis shocks depicted in Figure 7 could be analyzed in three phases. First
there was the run-up to the global financial crisis. During the period starting around 2005, the
positive contribution of the foreign demand shocks to growth starts gaining momentum. The
healthy growth rate of the global economy that solidified in 2005 certainly is one reason why
foreign demand seems to have supported Turkish growth during this period. Then, during the
last quarter of 2008, emerging markets started feeling the brunt of the global crisis.
According to the figure it was initially the financial uncertainty shock that negatively
impacted Turkish growth, followed by the foreign demand shock. The last phase corresponds
to the onset of the recovery lead by a decrease in the financial uncertainty shocks. We find
that the financial uncertainty shock explains a large fraction of the downturn among the three
crisis shocks. It is also interesting to note the lingering effects of the foreign demand shock.
The depressed growth trajectory in our main trading partner—the Euro area—surely
contributed these dynamics.
The growth contributions of the remaining supply and demand shocks are shown in Figure 8.
The two prominent supply shocks are the unit-root and investment-specific technology
shocks. In contrast to some other studies, there seems to be a limited role for the cost push
(markup) and stationary technology shocks. By contrast, the unit root technology shock
seems to be the most important of the supply shocks, echoing the result of Aguilar and
Gopinath (2007) who argue that these types of trend shocks are important determinants of
business cycle fluctuations across emerging markets. There also seems to be an important
contribution by the investment-specific technology shocks, a point made by Justiniano and
others (2010). The demand shocks consist of the government spending, preference, and time-
varying inflation target shock. The latter has a negligible role, and the remaining two demand
shocks usually tend to offset each other to varying degrees over time. Overall, we see that the
net effect of these shocks acted as a drag on growth, particularly in the early phase of the
global financial crisis.
17
VII. THE ROLE OF MONETARY POLICY DURING THE CRISIS
In this penultimate section of the paper, we conduct some counterfactual experiments with
the goal of answering the following question:
If the adoption of the flexible exchange rate regime and the implementation of active
countercyclical monetary policy within an inflation targeting framework were not
carried out, how much deeper would the recent recession been?
As will be discussed below, that answer is that the recession would have been significantly
more severe. In fact, the counterfactual experiments we discuss below indicate that the
countercyclical and discretionary interest rate cuts implemented by the CBRT within an
inflation targeting regime underpinned by a flexible exchange rate added at least 3.2
percentage points to the 2009 real GDP growth outturn.
Before proceeding, it may be useful to recall that after the 2001 financial crisis, two
monetary policy reforms were carried out: (1) the fixed and heavily managed exchange rate
regimes of the past were abandoned in favor of a flexible exchange rate, and (2) the CBRT
started implementing an inflation targeting regime—implicit initially, then officially as of
2006. Against this backdrop, while not the focus of the paper, as a by-product of our
modeling setup, we can also take a first pass at assessing the possible role of the post-2001
financial reforms. As discussed in Section II, with these reforms the risk profile of the
Turkish economy—lead by the banking sector—decreased markedly in the aftermath of the
2001 crisis. In terms of a summary indicator, consider the leverage ratio in Table 1. Based on
a cross section of firms, the average leverage ratio decreased to a value of two in 2007 from a
value of three in 2000. In an illustrative scenario we seek to quantify the role of these reforms
by altering the steady state leverage ratio.
Setting up the counterfactual simulations
Therefore in what follows, we consider four counterfactual simulations and compare them
with the actual realization which is our baseline. Under the baseline, the monetary policy
framework operates under a flexible exchange rate regime, follows the estimated baseline
interest rate rule which reacts to the output gap and allows for deviations from the rule (in the
form of the monetary policy shocks discussed above). In this context, the four counterfactual
experiments are as follows:
No monetary policy shocks: this counterfactual posits strict adherence to the
baseline empirical interest rate rule. It is a simulation that excludes the monetary
policy shocks, that is, the monetary policy shocks,
are all set to zero in this
simulation. It serves to address the following question: What would the dynamics of
output growth have been if the CBRT did not implement any discretionary policy
18
(deviations from the interest rate rule) during the crisis? While the previous section
answered this question, here we seek to underscore this result and provide further
context.
No response to the output gap: under this counterfactual, the output gap coefficient
in the empirical interest rate rule is set to zero (
). Furthermore, as these
counterfactuals are ―cumulative,‖ this scenario also sets the monetary policy shocks
to zero. It serves to address the following question: What would the dynamic of
output growth have been if the CBRT did not implement any discretionary policy and
did not take into consideration the state of the output gap when formulating its policy
decisions during the crisis?
Peg: in this counterfactual, the CBRT is assumed to implement a strict fixed
exchange rate regime.
7
Intuitively, monetary policy does not react to the output gap,
and there are no discretionary deviations from the rule (which solely focuses on
stabilizing the nominal exchange rate). Here we seek to address the following
question: What would the dynamic of output growth have been if the CBRT was
implementing a fixed exchange rate regime?
Peg with heightened financial vulnerability: under the last counterfactual, the
CBRT is presumed to operate under a fixed exchange rate regime as above, but the
leverage ratio is calibrated to correspond to the case where it equals three in line with
the value in Table 1 during 2000.
8
While not the main focus of the paper, out
modeling framework allows us to construct such an illustrative counterfactual serving
to address the following question: What would the dynamic of output growth have
been if the CBRT was implementing a fixed exchange rate regime and the economy
was financially more vulnerable?
Results based on the counterfactual simulations
Figure 9 depicts the level of real GDP with the first quarter of 2008 (the pre-crisis peak)
normalized to 100 to allow the reader to better distinguish the (cumulative) effects of each
7
Just as the model-based framework assumes that the inflation targeting regimes are fully credible, it also
assumes that the exchange rate regimes are fully credible. While the latter assumption is harder to justify, the
credibility of both regimes is needed for comparability. For a lack of a better term, credibility was used, but
perhaps sustainability is a more related or even more appropriate characterization.
8
Recall that the Turkish banking system was nearly completely overhauled after the 2001 crisis. Key
vulnerabilities including excessive leverage, maturity and currency mismatches which aggravated the severity
of the 2001 crisis declined markedly. Evidence suggests that this lower risk profile became widespread as
shown in the lower leverage ratios shown in Table 1, which indicates that the aggregate leverage ratio decreased
to 2.0 in 2007 from 3.0 in 2000. For these illustrative scenarios, we use these leverage ratios to calibrate the
risk profile of the entrepreneurs in our model economy.
19
counterfactual. The figure depicts (1) the actual realization of real GDP (the baseline
scenario), (2) the counterfactual scenario without the monetary policy shocks, (3) the
counterfactual scenario without the monetary policy shocks and with the output gap
coefficient in the empirical interest rate rule is set to zero, (4) the counterfactual scenario
with the fixed exchange rate regime (peg), and (5) an illustrative counterfactual scenario with
the peg under heightened financial vulnerabilities.
As clearly seen from Figure 9, the inflation targeting framework underpinned by a flexible
exchange rate regime clearly softened the impact of the global financial crisis. More
specifically, it is useful to discuss three main results:
First, as expected, output growth declines the most under the fixed exchange rate
regime. The lack of the exchange rate to serve as a shock absorber decreases the
resiliency of the economy to the shocks that ensued during the global crisis.
Intuitively, the illustrative counterfactual experiment with heightened financial
vulnerabilities, and thereby a more pronounced balance sheets channel, leads to an
even sharper decline in output. These counterfactual experiments highlight the role of
the exchange rate flexibility as well as financial reforms that promote the soundness
of the financial system.
Second, giving weight to the output gap seems to have a more limited role, but that is
to be expected as the estimated coefficient (of 0.02) is quite low. In other words, the
interest rate rule coefficient implies a small systematic response of policy rate to
output gap, and a large discretionary (nonsystematic) response as summarized by the
expansionary monetary policy shocks which we discuss next.
Third, as discussed in the previous section, there is an important role for the
discretionary departure from the interest rate rule, which helped soften the impact of
the crisis. At first glance, while they may seem small, as we discuss in further detail
in the next subsection, the role of these discretionary departures from the interest rate
rule (the monetary policy shocks) are very much in line with the literature.
While our results suggest that the inflation targeting framework underpinned by a flexible
exchange rate supported growth during the global financial crisis, clearly other policies also
played a role. For example, it should be noted that we do not capture the direct effects of the
liquidity measures enacted by the CBRT starting in the fourth quarter of 2008. Some of these
policies include extending the terms of repurchase (repo) transactions, restarting foreign
exchange auctions, and reducing reserve requirements on foreign exchange deposits (for
further details, see Yalcin and Thomas, 2010). Moreover, fiscal policy is modeled along the
lines of many other studies in this strand of the literature, and is admittedly cursory.
Therefore, it is important to recognize that it might be possible that some of the contributions
of expansionary fiscal policy and some of the liquidity measures implemented during the
20
crisis (and not directly captured by our model) could have been attributed to the monetary
policy shocks.
9
How do our results compare with those in the literature?
We now focus on the growth implications associated with the counterfactuals discussed
above. The main takeaways discussed above could have also been based using (year-over-
year, demeaned) growth rates as shown in Appendix Figure 7. However, this section
tabulates the precise contributions to growth under the various counterfactuals as shown in
Table 5. The intention is for the table to focus on the most intense period of the crisis, but
this could be open to interpretation. Therefore, in the context of the Turkish economy, we
consider two alternative crisis episodes: 2008:Q4–2009:Q4 or 2009:Q1–2009:Q4.
Before investigating the details, it would be useful to clarify the information contained in
Table 5. The values under columns show either the average or cumulative contributions to
growth during these two episodes. It presents our results, as well as the results of Christiano
and others (2007), the most closely related study to our in terms of conducting counterfactual
experiments. The number of quarters in each episode and the quarterly cut in interest rates
is also presented. Columns 1 through 5 indicate the incremental contribution to growth
owing to the consecutive implementation of each policy. For example, consider the
2009:Q1–2009:Q4 episode. Under Column 4 indicates that reducing financial vulnerabilities
added, on average, 1.45 percentage points to growth. In addition to this effect, the
incremental growth contribution of adopting a flexible exchange rate regime, denoted under
column 3, is 1.86 percentage points.
It would be useful to first compare the results in Table 5 with the literature. Turning our
attention to column 1, we see that the average contribution of the monetary shocks
(discretionary deviations from the empirical interest rate rule) to output growth of around one
percent (1.14 or 1.18 percent depending on the episode chosen) lies in between the values
found by Chrisitiano and others (2007) for the U.S. (0.75 percent) and the euro area
(1.27 percent). The cumulative growth contributions also seem reasonable, and give some
context on the role of monetary policy in terms of softening the impact of the crisis.
To more intuitively summarize the findings in the counterfactuals above, we focus on the
most intense year of the crisis, namely 2009. As shown in Table 6, the actual growth rate for
2009 was –4.8 percent. Our model-based simulations suggest that if the CBRT had not
departed from the empirical interest rate rule, growth would have instead been –5.9 percent, a
difference of 1.1 percentage points. Furthermore, if instead of the inflation targeting regime,
a peg was in place, the results imply a growth rate of –8.0 percent, a difference from the
actual of 3.2 percentage points. In sum, without the adoption of the flexible exchange rate
9
We thank Fatih Özatay for pointing out this possibility.
21
regime, and active countercyclical monetary policy guided by an inflation targeting
framework, the impact of the recent global financial crisis would have been substantially
more severe.
The appendix provides two other measures to gauge the severity of the recessions presented
in the counterfactual scenarios. First, using the level of GDP, we show the differences in the
peak-to-trough output contractions. Second, we consider the ―area under the curve,‖ whereby
the metric compares the annualized average output loss relative to the baseline. Both of these
alternative measures are quantified in Appendix Table 1. Overall, whatever metric one
prefers, it is clear that the adoption of an inflation targeting framework underpinned by a
flexible exchange rate regime helped soften the impact of the recent global financial crisis.
VIII. SUMMARY AND MAIN POLICY IMPLICATIONS
This paper develops and estimates a structural model using Turkish time series over the
2002–10 period corresponding to the Central Bank of the Republic of Turkey (CBRT)‘s
gradual transition to full-fledged inflation targeting. Turkey is an interest emerging economy
case study because it was one of the hardest hit countries by the crisis, with a year-over-year
contraction of 14.7 percent during the first quarter of 2009. At the same time, anticipating the
fallout from the crisis, the CBRT decreased policy rates by an astounding 1025 basis points
over the November 2008 to November 2009 period.
To this end, general question this paper seeks to address is the following: Did the monetary
policy implemented by the CBRT help soften the impact of the recent crisis? However, we
interpret monetary policy more broadly and therefore investigate the role of being able to
implement countercyclical monetary policy within an inflation targeting regime underpinned
by a flexible exchange rate regime. In this context, we seek to address the following
question: If an inflation targeting framework underpinned by a flexible exchange rate regime
was not adopted, how much deeper would the recent recession been? This paper finds that
the recession would have been substantially more severe.
This finding is based on counterfactual simulations derived from an estimated dynamic
stochastic general equilibrium (DSGE) model which includes a financial accelerator
mechanism in an open-economy framework. These counterfactual situations allow us to
quantify the differences in terms of growth between actual outcomes, and for example, a case
where the CBRT did not implement any countercyclical and discretionary interest rate cuts.
The most intuitive way to communicate our quantitative results is by taking the growth rate
during the most intense year of the global financial crisis, namely 2009, as our baseline. In
this context, our counterfactual simulations indicate that without the countercyclical interest
rates cuts implemented by the CBRT, growth in 2009 would have decreased from the actual
realization of –4.8 percent to –6.2 percent. Moreover, if a fixed exchange rate regime would
have been in place instead of the current inflation targeting regime (which is underpinned by
a flexible exchange rate), the results show that growth in 2009 would have been –8.0 percent,
22
a difference from the actual outcome of 3.2 percentage points. In other words, these
simulations underscore the favorable output stabilization properties owing to the combination
of countercyclical interest rate cuts (consistent with the inflation target) and exchange rate
flexibility.
In sum, without the adoption of an inflation targeting framework underpinned by a flexible
exchange rate regime, the impact of the recent global financial crisis would have been
substantially more severe.
23
APPENDIX
This appendix has four main sections providing further details regarding some of our main
results. First, we present a detailed description of the structural DSGE model that underpins
our quantitative results. The next two sections discuss model estimation and sensitivity
analysis, while the fourth section sheds further light on model dynamics, and the final section
presents the counterfactual simulations using the time series of year-over-year growth rates.
The Model
This section presents a detailed description of the dynamic stochastic general equilibrium
(DSGE) model that serves as our analytical framework. The model is an open economy New
Keynesian DSGE model equipped with additional features to better fit the data including a
number of nominal and real rigidities, a stochastic trend, and a financial accelerator
mechanism among others. Our model brings together elements from papers including
Adolfson and others (2007), Bernanke and others (1999), Elekdag and others (2006), aw well
as Gertler and others (2007) among many others.
The model consists of several agents including households, producers, and the government.
There are three types of producers: entrepreneurs, capital producers, and retailers. The
government is responsible for implement monetary and fiscal policy. A visual representation
of the flow of goods and services across these agents is shown in Figure 2. We consider the
role of each of these agents, and there interactions with the rest of the world in turn below.
Households
There is a continuum of households, which attain utility from aggregate consumption,
, and
leisure,
. Aggregate consumption is given by a CES index of domestically produced and
imported goods according to:
(1)
where
and
are the consumption of the domestic and imported goods, respectively.
Moreover, is the share of domestic good in consumption, and is the elasticity of
substitution between domestic and foreign consumption goods.
Intratemporal optimization by the household implies the following two conditions, the latter
being the consumer price index,
:
(2)
24
(3)
The households decide on their current and future level of consumption as well as their
amount of domestic and foreign bond holdings based on the following preference structure
which allows for habit persistence as captured by the term
:
(4)
where
and
are the preference and labor supply shocks, respectively, each having the
following first-order autoregressive (AR(1)) time series representations:
(5)
(6)
The representative household is assumed to maximize the expected discounted sum of its
utility subject to budget constraint:
(7)
where
denote the nominal wage,
real dividend payments (from ownership of retail
firms),
the nominal exchange rate,
and
nominal bonds denominated in domestic
and foreign currency, respectively, and
and
, the domestic and foreign gross nominal
interest rate, respectively. The foreign interest rate is an exogenous AR(1) process. In
addition,
represents a gross borrowing premium that domestic residents must pay to
obtain funds from abroad, specifically:
(8)
(9)
As in Gertler and others (2007), the country borrowing premium depends on total net foreign
indebtedness and an exogenous process,
, also modeled as an AR(1) process. The
introduction of this risk-premium is needed in order to ensure a well-defined steady state in
the model (see Schmitt-Grohe and Uribe, 2003, for further details).
The solution of the household‘s intertemporal utility maximization problem yields the
following Euler equation:
25
(10)
where
, the marginal utility of the consumption index, is given by:
(11)
In addition, the optimality condition governing the choice of foreign bonds yields the
following uncovered interest parity condition (UIP), where it is now clear that the exogenous
process,
, could be interpreted as a risk premium (UIP) shock:
(12)
As will be discussed below, shocks to the UIP condition are typically used to imitate a
sudden stop shock (in parlance of Calvo and other, 2004), that is a shock that is causes large
capital outflows (see, for example, Gertler and others, 2007). In the context of this paper, we
follow suit, and use this shock to capture the financial aspect of the global financial crisis.
Wage setting
Each household is be a monopolistic supplier of a differentiated labor service desired by the
domestic firms. This implies that each household has some pricing power over the wage it
charges,
. After having set their wages, households inelastically supply the firms‘ demand
for labor at the going wage rate.
Each household sells its labor services,
, to a firm which transforms household labor into
a homogeneous input good,
, using the following production function:
(13)
where
is the wage markup. This firm takes the input price of the differentiated labor input
as given, as well as the price of the homogenous labor services. The demand for labor that an
individual household faces is determined by:
(14)
Following Kollmann (1997) and Erceg and others (2000), we assume that wages can only be
optimally adjusted after some random ―wage change signal‖ is received. Households that do
not receive the "signal" update their previous period wage by indexing it to the current
26
inflation rate target,
, the previous period's inflation rate,
, and the current growth rate
of the technology level,
. More formally, a household who does not re-optimize in period t
sets its wage as:
(15)
where
is the degree of wage indexation, with
.
Household can re-optimize its wage according to the following dynamic program:
(16)
where
is the probability of not changing the wage rate. After inserting the relevant
expressions for
in the optimization problem, the following first order condition can be
derived:
(17)
where
is the marginal disutility of labor. The log-linearized real wage
equation, which is derived from the above equation, can be obtained as:
(18)
with
(19)
It should be noted that as shown in Figure 1, Turkey was plagued with high and persistent
levels of inflation which became entrenched. These nominal rigidities, that is the sticky
wages (and prices discussed below) clearly capture an important aspect of the Turkish
economy. Exacerbating the persistence stemming from the stick wages (and prices), it is also
assumed that indexation is prevalent across the economy. The estimation of the model would
help determine the importance of these features.
27
Foreign Economy
In considering arbitrage in goods markets, we distinguish between the wholesale (import)
price of foreign goods and its retail price in the domestic market by allowing for imperfect
competition and pricing-to-market in the local economy. Let
denote the wholesale price
of foreign goods in domestic currency,
the foreign currency price of such goods, and,
,
the nominal exchange rate. At the wholesale level, the law of one price holds, which implies:
(20)
Following Gertler and others (2007), we assume that foreign demand for the home tradable
good,
, is given by:
(21)
where
is real foreign output (or equivalently, foreign demand) and
is the foreign price
level, which are assumed to be exogenous AR(1) processes. The term
represents
inertia in foreign demand for domestic products, and
, where
is the retail
price of exported good in domestic currency. By now one can anticipate that a shock to
would capture the trade channel of the global financial crisis.
Entrepreneurs
The set up for entrepreneurs is similar to the framework in Gertler and others (2007), who
build upon the framework introduced by Bernanke and others (1999). Risk neutral
entrepreneurs manage production and obtain financing for the capital employed in the
production process. To ensure that they never accumulate enough funds to fully self-finance
their capital acquisitions, we assume they have a finite expected horizon. Each entrepreneur
survives until the next period with probability
, which is time-varying, and subject to an
exogenous shock. Intuitively, an adverse shock could be interpreted as an impairment of the
entrepreneurs assets caused by heighted financial uncertainty. Variations of this shock have
been used by Christiano and others (2003), Elekdag and others (2006), Curdia (2007), as well
as Christensen and Dib (2008).
At time t, the entrepreneur starts with capital,
, acquired in the previous period. He then
produces domestic output,
, using labor,
, and capital services,
, where
is the
capital utilization rate. The labor input
is assumed to be a composite of household and
managerial labor:
(22)
28
where
is managerial labor, which is assumed to be constant as in Bernanke and others
(1999). The entrepreneur‘s gross project output,
, consists of the sum of his production
revenues and the market value of his remaining capital stock. In addition, we assume the
project is subject to an idiosyncratic shock,
, with E
, that affects both the
production of new goods and the effective quantity of his capital.
(23)
where
be the nominal price of wholesale output,
the real market price of capital with
respect to household consumption index,
the price of investment good,
the
depreciation rate.
is denoted as the wholesale good production, which has the following
technology:
(24)
where
is a stationary productivity shock and
is permanent technology shock, which is
exogenously given by :
(25)
(26)
Following Greenwood and others (1988), we endogenize the utilization decision by assuming
that the capital depreciation rate is increasing in
. The depreciation rate,
, is a function of
the utilization rate taking the following form:
(27)
The problem of the entrepreneur is to choose labor and the capital utilization rate to
maximize
profits, given the values of
,
,
and
. The optimality conditions imply the following
labor demand functions:
(28)
(29)
where
is the managerial wage. The optimality condition for capital utilization is:
29
(30)
The entrepreneurs also make capital acquisition decisions. At the end of period t, the
entrepreneur purchases capital that can be used in the subsequent period t +1 to produce
output at that time. The entrepreneur finances the acquisition of capital partly with his own
net worth available at the end of period t,
, and partly by issuing nominal bonds,
,
which are purchased by the household. Then capital financing is divided between net worth
and debt, as follows (a standard balance sheet identity):
(31)
The entrepreneur‘s demand for capital depends on the expected marginal return and the
expected marginal financing cost. The marginal return to capital,
, is given by:
(32)
where ,
, depends on the next period‘s ex-post gross output net of labor costs,
normalized by the period t market value of capital. Here,
is the average level of output
per entrepreneur (
). Taking expectations, the equation above can be recast
as:
(33)
The marginal cost of funds to the entrepreneur depends on financial conditions. As in
Bernanke and others (1999), we assume a costly state verification problem. In this setting, it
is assumed that the idiosyncratic shock
is private information for the entrepreneur,
implying that the lender cannot freely observe the project‘s gross output. To observe this
return, the lender must pay an auditing cost—interpretable as a bankruptcy cost—that is a
fixed proportion of the project‘s ex-post gross payoff. Since the lender must receive a
competitive return, it charges the borrower a premium to cover the expected bankruptcy
costs. The external finance premium affects the overall financing cost, thereby influencing
the entrepreneur‘s demand for capital.
In general, the external finance premium varies inversely with the entrepreneur‘s net worth:
the greater the share of capital that the entrepreneur can self-finance, the smaller the expected
bankruptcy costs and, hence, the smaller the external finance premium. Then, the external
finance premium,
, may be expressed as:
30
(34)
Note that role played by
, the real price of capital, or perhaps more intuitively, the asset
price. The equation for external finance premium suggests that, through its effect on the
leverage ratio, the movements in real price of capital may affect the external finance
premium significantly. Therefore, this equation provides an explicit mechanism that captures
the link between asset price movements and variations in firms‘ cost of financing.
By definition, the entrepreneur‘s overall marginal cost of funds in this environment is the
product of the gross premium for external funds and the gross real opportunity cost of funds
that would arise in the absence of capital market frictions. Accordingly, the entrepreneur‘s
demand for capital satisfies the optimality condition:
(35)
This equation provides the basis for the financial accelerator. It links movements in the
borrower financial position to the marginal cost of funds and, hence, to the demand for
capital. Note, as mentioned above, that fluctuations in the price of capital,
, may have
significant effects on the leverage ratio.
The other key component of the financial accelerator is the relation that describes the
evolution of entrepreneurial net worth,
. Let
denote the value of entrepreneurial firm
capital net of borrowing costs carried over from the previous period. This value is given by:
(36)
Then, net worth is expressed as a function of
and the managerial wage.
(37)
where the weight
reflects the time-varying survival rate, which is a stochastic exogenous
process, specifically:
(38)
(39)
Here, the net worth shock,
, can be interpreted as a financial uncertainty shock since it has
direct impact on the level of aggregate net worth and therefore the external financial
31
premium. Put differently, the net worth shock could be interpreted as a shock to the rate of
destruction of entrepreneurial financial wealth. As is clear from above, this financial
uncertainty shock directly affects entrepreneurial net worth and has been used in various
forms by Elekdag and others (2006), Curdia (2007), Christiano and others (2003). Another
way to think about this shock is that it could be thought of capturing counterparty risk—
owing part to Knightian uncertainty—a key consideration during the global financial crisis.
This heightened uncertainty regarding cash flows, for example, would impair assets and thus
disrupt the financial system.
Lastly, entrepreneurs going out of business at time t consume their remaining resources.
Then the consumption of entrepreneur is given by:
(40)
where
denote the amount of the consumption composite consumed by the existing
entrepreneurs.
Capital producer
We assume that capital goods are produced by a separate sector in a competitive market.
Capital producers are price takers and owned by the representative households. At the end of
the period t, they buy the depreciated physical capital stock from the entrepreneurs and by
using total investment good, they convert them into capital stock, which is sold to
entrepreneurs and used for production at period t+1. Production technology is described by
the following evolution of capital:
(41)
where is the capital adjustment cost with properties :
and
is stationary investment-specific technology shock following an AR(1) process. Note
that only the parameter
is identified and will be used in the log-linearized model.
As with consumption, the total investment good is assumed to be given by a CES aggregate
of domestic and imported investment goods (
and
, respectively):
(42)
32
where
is the share of imports in investment, and
is the elasticity of substitution between
domestic and imported investment goods. Because prices of the domestically produced
investment goods coincide with the prices of the domestically produced consumption goods
we have the following investment demand function:
(43)
where the aggregate investment price,
, is given by:
(44)
The problem of capital producer is to maximize its future discounted profit stream:
(45)
subject to the evolution of capital, and implies the following first order condition:
(46)
Retailers of Domestic Good
We assume that there is a continuum of monopolistically competitive retailers of measure
unity. Retailers of domestic good buy wholesale goods from entrepreneurs in a competitive
manner at price
and then differentiate the product slightly and sell their output to
households, capital producers, and foreign country. Given that their output is differentiated,
retailers have the monopolistic power to set prices of these final output goods.
Let
be the good sold by retailer i. Final domestic output is a CES composite of
individual retail goods, given by:
(47)
where
is a stochastic process determining the time-varying markup which is assumed to
follow:
(48)
33
The cost minimization problem implies that each retailer faces an isoelastic demand for his
product given by:
(49)
where
is the price of retailer i and
is the corresponding price of the composite final
domestic good, given by:
(50)
In parallel to the problem considered for wage determination, the price setting decision in
retail sector is modeled as a variant of the Calvo (1983) framework with indexation. In this
setting, each retailer can re-optimize its price with probability
, independently of the
time elapsed since the last adjustment. With probability
, on the other hand, the retailer is
not allowed to re-optimize, and its price in period t+1 is updated according to the scheme:
(51)
where
.
Under these assumptions, the retailer of domestic good which is allowed to set its price,
, solves the following optimization problem when setting its price:
(52)
where
is fixed costs, in real terms, ensuring that the profits are zero in steady state and
.
Solving this problem, the following first-order condition is obtained:
(53)
34
From the aggregate price index discussed above follows that the average price in period t is:
(54)
where we have exploited the fact that all firms that re-optimize set the same price. Log-
linearizing and combining the previous two equations yields the following aggregate Phillips
curve relation:
(55)
Retailers of Imported Good
The import sector consists of a continuum of retailers that buy a homogenous good in the
world market, turn the imported product into a differentiated (consumption and investment)
good and sell it to the consumers and capital producers. Different importing firms buy the
homogenous good at price
. In order to allow for incomplete exchange rate pass-
through to the consumption and investment import prices, we assume local currency price
stickiness. In particular, similar to the domestic good retailer case, the importing firms follow
a Calvo (1983) price setting framework and are allowed to change their price only when they
receive a random price change signal with probability
. The firms that are not
allowed to re-optimize, update their prices according to the scheme similar to the domestic
retailer‘s case:
(56)
where
.
Let
denote the good sold by imported retailer i. Then, the final imported good (sum of
consumption and investment imported good) is a CES composite of individual retail goods,
given by:
35
(57)
where
is a stochastic process determining the time-varying markup for importing good
firms which is assumed to follow:
(58)
The cost minimization problem implies that each retailer faces an isoelastic demand for his
product given by:
(59)
where
denotes the price of retailer i and
is the corresponding price of the
composite final imported good, given by:
(60)
Under these assumptions, the profit maximization problem of the imported good firm which
is allowed to set its price is given by:
(61)
where
is fixed cost of the imported good firm and
.
The problem yields the following first-order condition:
(62)
36
The first order condition and aggregate price index for imported goods given above yield the
following log-linearized Phillips curve relation for imported good inflation;
(63)
Monetary Policy
In our model, we include a central bank that implements a general interest rate rule to
achieve specific policy objectives. The interest rate rule takes the following (log-linear) form:
(64)
where
denotes an independent and identically distributed domestic monetary policy shock.
The policy rule implies that the monetary authority sets the nominal interest rate, taking into
consideration the inflation rate deviation from the time-varying inflation target, the output
gap, the rate of exchange rate depreciation, and the previous period‘s interest rate. The
inflation target is assumed to evolve according to the following stochastic process:
(65)
Anticipating the results to follow, notice that when the output gap is negative—that is output
is below potential—strict adherence to the rule above would imply that the interest rate
decreases by an amount dictated by the coefficient
. However, the monetary authority
might decrease interest rates by more than what the systematic component of the rule would
imply. This deviation from the rule is capture by the error term, which is the monetary policy
shock. As will be discussed in further detail below, during the most intense episode of the
global financial crisis, interest rates decreased by more than the amount the empirical
counterpart of the rule would have implied.
Market Clearing Conditions
Finally, good market equilibrium is defined by the following equations:
(66)
where
is AR(1) exogenous spending process as in Smets and Wouters (2007).
In the model, all nominal variables are scaled by consumer price index,
, and all real
variables, except labor, are scaled by the real stochastic trend,
, in order to render the model
stationary. Then the model is log-linearized around its steady state.
37
Estimation
The log-linearized model is estimated using Bayesian methods primarily developed by
Schorfheide (2000), and later popularized by Smets and Wouters (2003, 2007). In what
follows, we discuss the data used in the estimation process, the calibration of the parameters
that pin down key steady state ratios, the prior and posterior distributions of the estimated
parameters, and then end with an assessment of the fit of the model.
Data
The model is estimated using quarterly data from the first quarter of 2002 to the second
quarter of 2010 using the series shown in Figure 3. In line with many other studies, we have
chosen to match the following set of twelve variables:
the levels of the domestic policy and
foreign interest rates, the inflation rates of domestic GDP deflator and core consumer price
and foreign consumer price indices, as well as the growth rates of GDP, consumption,
investment, exports, imports, foreign GDP, and the real exchange rate. This implies that we
derive the state space representation for the following vector of observed variables (shown
using model notation):
,
where, just to avoid any ambiguity,
, and,
, denote the growth rates of
exports and the real exchange rate, respectively. As is common in the literature, standard
transformations were needed to align the data with the model-based definitions. For example,
all interest rates are divided by four so that the periodic rates are consistent with the quarterly
time series. In addition, in order to make observable variables consistent with the
corresponding model variables, the data are demeaned by removing their sample mean, with
the exception of inflation and the interest rates, which are demeaned by subtracting their
steady-state values, A spreadsheet which contains our estimation dataset and shows in detail
all of our data transformations (including, for example, seasonal adjustment) is available
upon request.
As discussed above, the various regimes changes, structural breaks, and their attendant
effects on the Turkish time series suggests that utilizing a longer time span could yield
spurious inference. In fact, the sample period used for estimation covering the 2002–2010
period under consideration captures the episode when the CBRT was transitioned to an
inflation targeting regime (initially implicitly, and the explicitly starting in 2006).
Nonetheless, while the sample is clearly shorter than desired, other papers by prominent
authors have used samples in similar length including Del Negro and Schorfheide (2008).
Regarding the foreign variables, a weighted average of the time series from the United States
and the Euro area were used for real GDP, interest rate, and inflation rate. These two large
economies were chosen owing to data availability and because the Euro area is Turkey‘s
largest trading partner—the destination of over fifty percent of Turkish exports—and the
38
United States is where the recent global financial crisis originated. We tried various other
combinations, including, for example, using just the time series from the United States, and
found that our main results do not change noticeably.
Calibrated parameters
We followed the literature and calibrate certain parameters (see, for example, Christiano and
others, 2010), which could be thought of as infinitely strict priors. Many of the parameters
are chosen to pin down key steady state ratios, while the remaining parameters are taken
from the literature as summarized in Table 2.
To this end, we chose the values of , and
i
, to calibrate the consumption-, investment-,
government expenditures-, and exports-to-GDP ratios to the values of 70, 20, 10, and
24 percent, respectively. The parameter was fixed at 0.9928 implying an annual riskless
real interest rate of approximately three percent, close to many other studies in the literature.
Regarding the calibration of the financial accelerator, we wanted to fit the leverage ratio of
two for the year 2007 shown in Table 1 and the average EMBI spread of 300 basis points,
over the 2004-2010 period which excludes the immediate aftershocks of the 2001 financial
crisis. To achieve these steady state values, the parameters for the entrepreneurial survival
rate, the monitoring cost fraction, and the variance of the shocks to entrepreneurial
productivity were chosen to be 0.9728, 0.15, and 0.40, respectively.
The remaining calibrated parameters were taken from the literature. For example, the share
of entrepreneurial labor is set at 0.01 as in Bernanke and others (1999). The steady state price
and wage markups were chosen to be 15 percent, which lies in the 10 to 20 percent range
utilized in many other studies. The remaining parameters were based off Gertler and others
(2007) and include various elasticities of substitution summarized in Table 2.
Prior distributions of the estimated parameters
The remaining 43 parameters, shown in Table 3, are estimated. These parameters determine
the degree of the real and nominal rigidities, the monetary policy stance, as well as the
persistence and volatility of the exogenous shocks. The table shows the assumptions
pertaining to the choice of distribution, the means, standard deviations, or degrees of
freedom.
The choice of priors is in line with the literature. General principles guiding the choice of the
distributions are as follows: For parameters bounded between zero and unity, the beta
distribution was used. For those assumed to take on positive values only (standard
deviations), the inverse gamma distribution was used. Lastly, for unbounded parameters, a
normal distribution was chosen.
39
It may also be useful to compare our choices of prior means for some parameters across some
selected papers. For the Calvo (1983) parameters, we set the mean of the prior distribution to
0.5 as in Teo (2009). Similarly, the indexation parameter is set to 0.5 as well, as in Adolfson
and others (2007). Turning to the baseline monetary policy rule, interest rate persistence
takes a value of 0.7, which is in line with Elekdag and other (2006). In line with the Taylor
principle, the responsiveness to inflation was set at 1.4, slightly lower than in other studies,
including, for example, Garcia-Cicco (2010). The habit persistence parameter is chosen to be
0.7 as in Adolfson and other (2007), whereas the investment adjustment cost parameter is
relatively lower. Turning finally to the shocks, the persistence parameter was set at 0.8, lower
than in Adolfson and other (2007) who use 0.85, but higher than Elekdag and other (2006) as
well as Garcia-Cicco (2010), both of which use 0.5. Lastly, the priors guiding most of the
standard deviations of the shocks are based on an inverse gamma distribution, typically
centered around 0.03 with one degree of freedom.
Posterior distributions of the estimated parameters
The posterior estimates of the variables are also shown in Table 3. The table reports the
means along with the 5
th
and 95
th
percentiles of the posterior distribution of the estimated
parameters obtained through the Metropolis-Hastings sampling algorithm. The results are
based on a total of 500,000 draws and two independent chains, and the Brooks and Gelman
(1998) convergence criteria are achieved.
Additional information on our estimation results is presented in Appendix Figure 1a through
Appendix Figure 1c, which plot the kernel density estimates for the posteriors, together with
the priors for the estimated parameters. These kernel density estimates provide a visual
summary indicating that the data are quite informative regarding most of the estimated
parameters.
In general, the parameter estimates are in line with those found in other studies. While
comparing parameter estimates across studies is potentially useful, three important issues
should be kept in mind. First, various studies consider distinct countries. For example,
Garcia-Cicco (2010) considers Mexico (which exports a sizable amount of oil), Elekdag and
others (2006) investigate Korea, Teo (2009) focuses on Taiwan, and Adolfson and other
(2008) examine Sweden, not to mention closed-economy counterparts focusing on the United
States and the euro area as done in Christiano and others (2008). Second, just as the structural
features of the economies investigated are different, sample periods and the choice of time
series used also differ. For example, this paper deliberately includes the arguably most
intense periods of the recent global financial crisis, while most (if not all) other studies do
not. Third, while most of the models build upon a common core, important differences still
remain, most relevantly, for example, in the choice of the monetary policy rule used. In sum,
modeling, sample period, and data differences should be recognized when comparing
posterior estimates across various studies.
40
We now compare some selected posterior estimates with those found in some other estimated
open economy models. Starting off with nominal rigidities, we find the wage-Calvo
parameter of 0.6, which implies that wages are adjusted on average every 2.5 quarters. By
contrast to wages and imports prices, domestic prices seem to adjust every 1.5 quarters.
Relatedly, the parameters dictating the degree of indexation are found to be in the 0.5–0.6
range, implying that the Philips curve have significant backward looking components. These
findings are quite close to those presented by Adolfson and other (2007) and Teo (2009). As
for the real rigidities, the estimates regarding habit formation and investment adjustment
costs are 0.9 and 3.6, respectively. Regarding the former, Garcia-Cicco (2010) finds an
estimate of 0.8, and as for the latter, Teo (2009) estimates the parameter to be 3.2.
Comparison of estimated policy rules is much more challenging because various studies
focus on substantially different specifications. For example, Teo (2009) uses a money-based
postulation, whereas Adolfson and others (2007) include the real exchange rate, as well as
output growth and the change in inflation along with the more typical output gap and
deviation of inflation from target. With these considerations in mind, we first discuss the
interest smoothing parameter which is found to be 0.7, in line with many other studies. As for
the responsiveness of inflation deviation from target, our estimate is 1.5 (in line with the
original proposal by Taylor, 1993), which is close to the value of 1.6 found by Adolfson and
others (2007). The responsiveness to the nominal exchange rate depreciation is smaller
echoing the findings of Elekdag and others (2006). The responsiveness of policy rates to the
output gap takes on an even lower value of 0.02. It should be noted that although the interest
rate rule coefficient implies a small systematic response of policy rate to output gap, it will
be shown that CBRT responded to the large output drops during the crisis through
discretionary departures from the rule.
Turning to the exogenous shocks, we start off by discussing persistence. The estimated
persistence parameters lie within the range of 0.49 for the unit root technology shock, and
0.93 for the foreign demand shock. The 95
th
percentile of this shock persistence parameter is
estimated to be 0.96 indicating an absence of unit roots in these processes. The results also
echo the general finding in Adolfson and others (2007) which is that the persistence
parameters in our open economy setup are typically below those in close economy
frameworks like Smets and Wouters (2003). As for standard deviations, the foreign interest
rate shock is the least volatile, whereas the variability of the preference and investment
shocks are noteworthy. It may also be useful to point out that as in other studies, the unit-root
technology shock is more volatile than the stationary technology shock. As we will discuss in
further detail below, in terms of driving the business cycle, we shall see that the unit-root
technology shocks plays a much more prominent role, which is consistent with the theoretical
predictions of Aguiar and Gopinath (2007).
41
An initial assessment of model fit
In terms of assessing the fit of the model, we start off by comparing the data with the
baseline model‘s one-sided Kalman filter estimates of the observed variables, and then
consider model robustness in the following section. The data and the filtered variables are
shown in Figure 3 indicating that the sample fit is generally satisfactory.
First note that the model and data track each other well with respect to the interest rate and
inflation rate series. The model is able to capture the downtrend in these variables during the
disinflation process in Turkey over our estimation sample. In fact, the correlations between
the model and the data for the inflation and interest rates are over 0.95 and 0.99, respectively.
Turning to the real variables, the model‘s consumption and investment predictions closely
mimic the data (with correlation coefficients exceeding of 0.97 and 0.99, respectively).
However, the fit of the model deteriorates for some other variables. In particular, the actual
evolution of exports markedly differs from what the model predicts. Recall that our sample
intentionally includes the recent global financial crisis including the attendant collapse in
global trade. Intuitively, the model‘s external linkages seem to have difficulty replicating the
unprecedented nature of this period. Also, Turkey‘s main export destinations have been
changing relatively rapidly more recently. Exporters have been quite successful in
diversifying across destinations and penetrating new markets (for example, the Middle East
and North Africa). The (unstable) interaction between exports and the real exchange rate also
could be a culprit. This appears to be related to the exchange ‗disconnected‘ puzzle discussed
in by Devereux and Engel (2002) as well as Duarte and Stockman (2005).
Sensitivity Analysis
As discussed in the main text, we investigate the importance of the various features of the
model by either reducing the degree of certain nominal and real frictions, omitting a shock
process, or evaluating another policy rule. Using the posterior odds ratio as our decision
metric, the baseline model seems to outperform the other competing models. As summarized
in Table 4, we consider 18 alternative specifications, and in all but one case, the results are
decisively in favor of the baseline, whereas in the remaining case, the results very strongly
favor the baseline. By way of interpreting the posterior odds ratios, we adapt the guidance
provided by Jeffreys (1961) which suggests that a ratio in the 10–100 range provides strong
to very strong evidence, and ratios above 100 provide decisive evidence in favor of our
baseline model. In this context, several results are worth emphasizing.
First, the exclusion of the financial accelerator mechanism is decisively rejected in
favor of the baseline model, which underscores the importance of incorporating such
financial frictions in models, particularly when investigating emerging markets, a
result also discussed extensively in Elekdag and others (2006), and later in Garcia-
Cicco (2010).
42
Second, the baseline is favored to models with low nominal and real rigidities. In
other words, as compared to the canonical real business cycle or New Keynesian
models, other features are needed to better fit the data.
Third, turning our attention to the role of structural shocks, the table indicates the
importance of technology shocks. Aguilar and Gopinath (2007) have argued for the
importance of trend shocks, Smets and Wouters (2003) noted the role of labor supply
shocks, and Justiniano and others (2007) find a critical role of investment-technology
shocks in accounting the variability of output dynamics. The sensitivity analysis
confirms the insight of these previous studies.
Fourth, demand shocks also seem to be important, as models without preference of
government spending shocks are rejected in favor of the baseline. As expected, the
baseline model is decisively chosen in contrast to a specification where the financial
shocks (financial uncertainty and the UIP shocks) are eliminated. In line with Ireland
(2007), we also consider a model where the persistence parameter of the time-varying
inflation target is set to unity; once again the results are decisively in favor of the
baseline specification.
Fifth, we consider five alternative monetary policy rules. In sum, the results are at
least very strongly in favor of the baseline specification. As shown in the table, as in
Smets and Wouters (2003), the change in output and the inflation rate is added, the
nominal depreciation rate is dropped, or a combination thereof. We also consider
cases without interest rate smoothing, strict inflation targeting, and lastly, a fixed
exchange rate regime. Especially regarding the later, the results are decisively in
favor of the model with a baseline interest rate rule.
It should be underscored that these rules are empirical interest rate rules and do not represent
the exact reaction functions of the monetary authority. That being the case, as in other studies
(Lubik and Shorfheide, 2007, Elekdag and others, 2006, as well as Adolfson and others,
2007), it seems that an exchange rate term is preferred by the data, which may serve as a
proxy capturing the forward-looking nature of monetary policymakers.
Model dynamics
As mentioned in the main text, the shocks are categorized into three groups. The first group
consisted of the monetary policy shock which was discussed extensively. In what follows we
investigate the impulse responses of the other two groups, which include the crisis shocks
and the other supply and demand shocks.
We first consider shocks to the UIP condition. Here we have two choices: we could
either focus on foreign interest rate shocks, or an exogenous shock to the condition
itself. In our model, the foreign interest rate shock is pinned down by the foreign
43
interest rate data used in the estimation process. Nonetheless, the estimated
persistence coefficients of the two shocks are similar, and therefore the impulse
responses related to these shocks are alike as shown in Appendix Figure 2a and
Appendix Figure 2b. For the purposes of this paper, because it matches the narrative
more closely, the first ―crisis‖ shock we focus on is the exogenous shocks to the UIP
condition, which we also refer to as the risk premium shock. In what follows, while
we focus on the baseline model—with an operational financial accelerator and
flexible exchange rates—we also present the dynamics under a fixed exchange rate
regime (sometimes referred as a peg) and when the financial accelerator mechanism
is shut off.
In the case of the risk premium shock, it might be useful to first start off discussing
the dynamics under the fixed exchange rate regime. As shown in Appendix Figure 2a,
the shock to the UIP condition results in an increase in the domestic nominal interest
rate. Because prices are sticky, this translates into an increase in the real interest rate,
thereby leading to a contraction. As expected, the financial accelerator amplifies the
initial impact of this shock. The rise in the real interest rate causes asset prices to fall,
which raises the leverage ratio and thus the external finance premium. The increase in
the latter further depresses investment, and therefore output. Under the baseline, the
depreciation of the exchange rate helps offset some of the adverse effects of the
shock—in other words, the flexibility of the nominal exchange rate serves as a shock
absorber. Real interest rates do not display a spike and the real exchange rate actually
depreciates by more, both which serve to soften the impact of the shock. Intuitively,
under the case with flexible exchange rates and no financial accelerator, the shock has
an even smaller perverse effect on output.
The shock to foreign demand is shown in Appendix Figure 3a. A foreign demand
shocks is essentially a decrease in exports and therefore directly depresses aggregate
demand. As discussed in Devereux and others (2006), such income shocks are similar
to terms of trade shocks as we will highlight further below. Under the baseline, the
sudden nominal exchange rate depreciation is the main driver of the real exchange
rate in the short-term, and helps mitigate the impact of the shock.
The financial uncertainty shock impairs the balance sheet of the entrepreneur by
eroding the value of net worth and is shown in Appendix Figure 3b. The sudden
decline in the value of equity causes an increase in the leverage ratio, and therefore
the external finance premium, thereby generating a decline in investment and output.
While the shock is estimated to be modestly persistent (with an autoregressive
coefficient of 0.72), the balance sheet of the entrepreneur evolves gradually adding to
the protracted nature of this shock. Another interesting feature of this shock is that it
converts the financial frictions from an accelerator to an attenuator because of
endogenous counter-cyclical adjustments as discussed in Christiano and others
(2010). This point is also made by Christensen and Dib (2008) who state that the
44
effect of the financial accelerator on output and investment fluctuations depends on
the nature of the shocks. Iacoviello (2005)—using a framework based on Kiyotaki
and Moore (1997) rather than Bernanke, Gertler, and Gilchrist (1999)—illustrates
these differences using nominal debt contracts whereby a supply shock, by increasing
prices, decreases the real value of debt (the debt deflation channel) which serves to
dampen the effects of supply shocks. Also note that because the fixed exchange rate
is associated with a lower real interest rate and small appreciation of the real
exchange rate, the contraction in output is not as severe as under the baseline.
It might also be useful to discuss some of the other supply and demand shocks in the
model. We first consider a shock to trend growth, also referred to as a unit-root
technology shock. Aguilar and Gopinath (2007) argue that this type of trend shock us
a key determinant of business cycle fluctuations across emerging markets. As shown
in Appendix Figure 4a, because the shock permanently affects the trend, the output
gap settles at a permanently lower level. Owing to the permanent nature of the shock,
household consumption decreases and is associated with a real exchange rate
depreciation (see, for example, Teo, 2009). Under the peg, the nominal exchange rate
does not depreciate by definition and thus the decrease in inflation is greater. This
situation is characterized by a larger rise in the real interest adding to the decline in
output.
Next we consider a stationary technology shock shown in Appendix Figure 4b. This
is the benchmark supply shock which has been attributed a pivotal role in the real
business cycle literature as it has been documented to be an important source of
business cycle fluctuations in open economies. As shown, the output gap widens
while inflation increases—the intuitive textbook negative correlation between (the
changes in) output and prices. By contrast to its permanent counterpart discussed
above, the stationary technology shock induces households to smooth consumption
fluctuations by borrowing internationally, which is characterized by the appreciation
of the exchange rate.
The model contains five other supply shocks, but because of similarities, here we
focus on two interesting cases. There are three reasons: brevity, similarity of the
impulse responses, and because some shocks have a negligible role in terms of
contributing to growth (for example that markup shock affecting importers). To start
off, consider the domestic markup shock popularized by Smets and Wouters (2003)
shown in Appendix Figure 5a. Casual observation quickly reveals that it is
remarkably similar to the stationary technology shock. One difference is that under
the baseline, the external finance premium increases thereby making the output
contraction slightly more severe than in the case with the financial accelerator
operational. The model is also buffeted with a labor supply shock, but this is not
shown because of the resemblance to the domestic markup shock. We also consider a
foreign cost push shock as depicted in Appendix Figure 5b. With the risk of
45
oversimplification, this could be thought of as a terms of trade (oil price) shock.
Notice that the dynamics are remarkably similar to those of the foreign demand
shock. This is because, as discussed above and noted by Devereux and others (2006),
terms of trade shocks bear semblance to income shocks to the external sector.
10
Lastly, we turn to the two remaining demand shocks. Appendix Figure 6a and
Appendix Figure 6b depict the government spending and preference shocks. In
general, contractionary demand shocks puts downward pressure on real marginal
costs and thereby inflation. However, as shown in the figures, the choice of exchange
rate regime has an important bearing on the final outcomes of exchange and interest
rates as well as the external finance premium.
11
Growth counterfactual
While the main text discussed the counterfactual scenarios using levels of real GDP, we
could have also conveyed the same policy messages using the (demeaned, year-over-year)
growth rates under the baseline (actual) and counterfactuals simulations as shown in
Appendix Figure 7. In sum, without the adoption of an inflation targeting framework
underpinned by a flexible exchange rate regime, the impact of the recent global financial
crisis would have been substantially more severe. It is worth emphasizing that while the
growth rates under the fixed exchange rates regimes are quite high in the last few quarters of
our sample, this is a main a results of base effects. In particular, as shown in Figure 9, the
level of economic activity is substantially depressed under the fixed exchange rate regimes,
thereby the year-over-year growth rates include this base effect and should accordingly be
carefully interpreted.
10
Given the negligible role in terms of contributing to growth, we do not discuss the markup shock that affects
the importers (however, the results are available from the authors upon request).
11
Given the negligible role in terms of contributing to growth, we do not discuss the inflation target shock
(however, the results are available from the authors upon request).
46
Table 1. Turkey: Financial Ratios Across Selected Industries
1
Sources: CBRT;
1
CR, ATO, NI, NS, ROA, and ROE denote the cash ratio, total asset turnover, net income, net sales, and return
on assets and equity, respectively. Leverage is defined as total assets over equity and NI/NS is the net profit
margin. Tabulated values denote industry averages. Averages across all sectors denoted with "All". Descriptive
statistics for major sector shown are below each section of the table.
2007 Value added Firms CR ATO Leverage NI/NS ROA ROE
All 7,352 140.2 1.0 2.01 5.3 5.1 10.3
Agriculture 7.3 48 174.6 1.0 2.21 5.1 4.1 7.1
Manufacturing 16.6 3,530 164.4 1.3 2.23 3.5 4.2 10.0
Construction 4.8 733 135.0 0.5 2.97 6.7 2.4 12.2
Wholesale/retial Trade 12.1 1,662 145.7 2.1 2.87 1.8 3.4 12.1
Transportation/communication 13.7 360 142.9 1.6 2.45 3.8 3.6 11.7
FIRE/Public administration 21.8 239 175.5 0.6 1.83 20.6 4.0 9.1
Mean 1,095 156.3 1.2 2.43 6.9 3.6 10.4
Median 547 155.0 1.2 2.34 4.4 3.8 10.9
Standard deviation 1,323 17.4 0.6 0.43 6.9 0.6 2.0
2000 Value added Firms CR ATO Leverage NI/NS ROA ROE
All 7,537 114.6 2.7 2.97 0.6 1.5 4.6
Agriculture 9.9 96 135.1 2.0 2.55 0.1 1.8 8.8
Manufacturing 20.1 3,901 139.7 1.7 2.56 2.7 3.8 13.0
Construction 5.0 1,004 106.2 1.0 3.85 5.7 3.0 20.1
Wholesale/retial Trade 12.7 1,436 125.5 3.1 3.41 1.7 4.8 22.2
Transportation/communication 12.2 338 113.2 2.3 2.48 0.1 -0.2 7.7
FIRE/Public administration 22.7 154 162.6 1.6 1.81 10.5 8.3 17.8
Mean 1,155 130.4 1.9 2.78 3.5 3.6 14.9
Median 671 130.3 1.8 2.55 2.2 3.4 15.4
Standard deviation 1,445 20.2 0.7 0.73 4.0 2.9 6.0
47
Table 2. Calibrated Parameters
Parameter
Symbol
Value
Discount factor
0.9928
Consumption intra-temporal elasticity of substitution
1
Share of domestic goods in consumption
0.75
Investment intra-temporal elasticity of substitution
0.25
Share of domestic goods in investment
0.77
Inverse of the elasticity of work effort with respect to the real wage
1
Share of capital in production function
0.4
Elasticity of marginal depreciation with respect to utilization rate
1
Steady state markup rate for domestically produced goods
1.15
Steady state markup rate for imported goods
1.15
Steady state markup rate for wages
1.15
Share of entrepreneurial labor
0.01
Steady state external finance premium
1.03
Number of entrepreneurs who survive each period (at steady state)
0.9728
Variance of idiosyncratic shock to entrepreneur production
0.4
Fraction of monitoring cost
0.15
Depreciation rate (at steady state
0.035
Elasticity of country risk premium with respect to net foreign debt
0.01
48
Table 3. Prior and Posterior Distributions
Parameter
Prior distribution
Posterior distribution
Description
Symbol
Type
Mean*
Standard
deviation
Mean
Confidence interval
5%
95%
Calvo parameter
Domestic prices
Beta
0.50
0.10
0.306
0.177
0.435
Import prices
Beta
0.50
0.10
0.554
0.434
0.679
Wages
Beta
0.50
0.10
0.558
0.387
0.739
Indexation
Domestic prices
Beta
0.50
0.10
0.460
0.296
0.623
Import prices
Beta
0.50
0.10
0.475
0.313
0.641
Wages
Beta
0.50
0.10
0.562
0.400
0.731
Others
Export demand elasticity
Normal
1.00
0.20
0.182
0.090
0.271
Export demand inertia
Beta
0.50
0.20
0.883
0.794
0.979
Habit formation
Beta
0.70
0.20
0.904
0.850
0.958
Investment adjustment cost
Normal
4.00
0.50
3.625
2.769
4.511
Monetary policy
Interest rate smoothing
Beta
0.70
0.20
0.724
0.639
0.808
Inflation response
Normal
1.40
0.10
1.537
1.383
1.685
Output gap response
Normal
0.25
0.10
0.021
-0.022
0.064
Nominal exchange rate response
Normal
0.10
0.05
0.173
0.101
0.242
Shock persistence
Stationary technology
Beta
0.80
0.10
0.767
0.593
0.939
Unit root technology
Beta
0.80
0.10
0.497
0.386
0.604
Investment specific technology
Beta
0.80
0.10
0.916
0.893
0.940
Domestic markup
Beta
0.80
0.10
0.774
0.609
0.945
Import markup
Beta
0.80
0.10
0.713
0.545
0.881
Foreign inflation
Beta
0.80
0.10
0.623
0.459
0.792
Foreign interest rate
Beta
0.80
0.10
0.847
0.751
0.948
Country risk premium
Beta
0.80
0.10
0.895
0.836
0.956
Foreign demand
Beta
0.80
0.10
0.928
0.892
0.962
Preference
Beta
0.80
0.10
0.721
0.556
0.888
Labor supply
Beta
0.80
0.10
0.762
0.605
0.931
Exogenous spending
Beta
0.80
0.10
0.868
0.791
0.944
Net worth
Beta
0.80
0.10
0.715
0.574
0.856
Inflation target
Beta
0.80
0.10
0.769
0.620
0.931
Shock volatility
Stationary technology
Inverse gamma
0.03
1.00
0.015
0.009
0.022
Unit root technology
Inverse gamma
0.03
1.00
0.047
0.036
0.057
Investment specific technology
Inverse gamma
0.03
1.00
0.421
0.334
0.504
Domestic markup
Inverse gamma
0.03
1.00
0.020
0.009
0.030
Import markup
Inverse gamma
0.03
1.00
0.052
0.032
0.072
Foreign inflation
Inverse gamma
0.03
1.00
0.008
0.007
0.010
Foreign interest rate
Inverse gamma
0.03
1.00
0.005
0.004
0.005
Country risk premium
Inverse gamma
0.03
1.00
0.011
0.007
0.015
Foreign demand
Inverse gamma
0.03
1.00
0.063
0.048
0.077
Preference
Inverse gamma
0.30
1.00
0.429
0.218
0.642
Labor supply
Inverse gamma
0.03
1.00
0.016
0.007
0.024
Exogenous spending
Inverse gamma
0.03
1.00
0.046
0.036
0.055
Net worth
Inverse gamma
0.05
1.00
0.061
0.035
0.086
Inflation target
Inverse gamma
0.02
1.00
0.012
0.005
0.020
Monetary policy
Inverse gamma
0.03
1.00
0.007
0.005
0.008
* For the inverse gamma distribution, the mean and the degrees of freedom are reported in the table.
49
Table 4. Sensitivity Analysis
Is the
Log
Posterior
Alternative
Data
Odds
Model
Density
Ratio
Superior?
Baseline
802.482
Sensitivity to frictions
1
Financial accelerator
779.963
6.025E+09
No
2
Low price stickiness
787.606
2.888E+06
No
3
Low stickiness including wages
726.449
1.049E+33
No
4
Low habit persistence
719.789
8.193E+35
No
5
Low investment costs
753.752
1.456E+21
No
Sensitivity to shocks
6
Technology (all)
619.305
3.573E+79
No
7
Labor supply
706.645
4.185E+41
No
8
Investment-specific technology
664.178
1.161E+60
No
9
Preference
664.068
1.296E+60
No
10
Government
699.794
3.951E+44
No
11
Financial (uncertainty and UIP)
796.283
4.924E+02
No
12
Unit root inflation target
794.754
2.272E+03
No
Sensitivity to policy rules
13
Add change in output and inflation
798.223
7.073E+01
No
14
796.913
2.622E+02
No
15
Same
792.821
1.570E+04
No
16
No interest rate smoothing
779.072
1.469E+10
No
17
Strict inflation targeting
776.368
2.193E+11
No
18
Fixed exchange rate regime
752.835
3.644E+21
No
Source: .
50
Table 5. The Role of Monetary Policy and Financial Reforms
(In percent)
[ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]
Cut Responsiveness Flexible
in Monetary to the exchange Reduced All
policy policy output rate financial factors
Quarters
rate shocks gap regime vulnerability
Average
5 1.98 1.18 0.32 2.22 1.69 5.42
4 2.40 1.14 0.35 1.86 1.45 4.81
Christiano and others (2008)
United States (2001Q2-2002Q2) 4 0.75
Euro area (2001q4-2004q4) 13 1.27
Cumulative
5 9.90 5.92 1.60 11.11 8.44 27.08
4 9.59 4.56 1.42 7.45 5.81 19.23
Christiano and others (2008)
United States (2001Q2-2002Q2) 4 3.00
Euro area (2001q4-2004q4) 13 17.00
Growth contributions of monetary policy owing to:
51
Table 6. Measuring the Severity of Economic Contractions
(In percent; calculations relative to actual 2009 annual real GDP growth)
.
Cumulative
Growth Difference Difference
Baseline (actual)
No monetary policy shocks
No response to output gap
Fixed exchange rate regime (peg)
Peg with heightened financial vulnerability
52
Figure 1. Turkey: Selected Macroeconomic Indicators
1
(Year-over-year growth rates and levels)
Sources: CBRT; Bloomberg; and authors calculations.
1
Y, C, I, INT, INF, REER, and TB/Y denote real GDP, real consumption, real investment, overnight interest rates, quarterly
inflation rates, real effective exchange rate and the trade balance-to-GDP ratio. EMBI represents JP Morgan's EMBI+ series for
Turkey (in basis points). The series with an asterisk represent foreign variables. Y,C, I, and Y* were all seasonally adjusted as
was the CPI series used to derive the inflation rates. Y,C, I, Y*, and the REER were logged before the seasonal adjustment,
and then their year-over-year growth rates were calculated.
-60
-50
-40
-30
-20
-10
0
10
20
30
40
50
-15
-10
-5
0
5
10
15
1988 Q1 1991 Q1 1994 Q1 1997 Q1 2000 Q1 2003 Q1 2006 Q1 2009 Q1
Y C I (Right)
-6
-4
-2
0
2
4
6
8
-50
-40
-30
-20
-10
0
10
20
30
40
1988 Q1 1991 Q1 1994 Q1 1997 Q1 2000 Q1 2003 Q1 2006 Q1 2009 Q1
REER TB_Y (Right)
0
200
400
600
800
1000
1200
0
50
100
150
200
250
1988 Q1 1991 Q1 1994 Q1 1997 Q1 2000 Q1 2003 Q1 2006 Q1 2009 Q1
INF INT EMBI (Right)
-4
-2
0
2
4
6
8
10
12
-4
-2
0
2
4
6
8
10
12
1988 Q1 1991 Q1 1994 Q1 1997 Q1 2000 Q1 2003 Q1 2006 Q1 2009 Q1
Y* INF* INT*
53
Figure 2. Model Schematic
Source: A calculations.
54
Figure 3. Model Predictions versus the Data
1
Source: Authors' calculations.
1
Data in thick black versus filtered thin lines with circles, see text for further details.
-8%
-6%
-4%
-2%
0%
2%
4%
6%
2002Q2 2004Q2 2006Q2 2008Q2 2010Q2
Data
Filtered
Output
-8%
-6%
-4%
-2%
0%
2%
4%
6%
2002Q2 2004Q2 2006Q2 2008Q2 2010Q2
Consumption
-30%
-20%
-10%
0%
10%
20%
2002Q2 2004Q2 2006Q2 2008Q2 2010Q2
Investment
-2%
-1%
0%
1%
2%
3%
4%
5%
6%
2002Q2 2004Q2 2006Q2 2008Q2 2010Q2
Inflation (core)
-2%
0%
2%
4%
6%
8%
10%
12%
2002Q2 2004Q2 2006Q2 2008Q2 2010Q2
Interest rate
-10%
-5%
0%
5%
10%
15%
20%
25%
30%
2002Q2 2004Q2 2006Q2 2008Q2 2010Q2
Real exchange rate
-15%
-10%
-5%
0%
5%
10%
15%
2002Q2 2004Q2 2006Q2 2008Q2 2010Q2
Exports
-25%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
2002Q2 2004Q2 2006Q2 2008Q2 2010Q2
Imports
55
Figure 4. Dynamic Responses to a Monetary Policy Shock
1
Source: Authors' calculations.
1
Interest rates, inflation rates, and the external finance premium are shown as absolute deviations from their
steady states, while the other variables are percentage deviations from their steady states.
Output gap
Inflation (year-over-year) Nominal interest rate
Real exchange rate (increase indicates appreciation) External finance premium Real interest rate
Imports Real price of capital Consumption
Exports Net worth Investment
-0.70%
-0.60%
-0.50%
-0.40%
-0.30%
-0.20%
-0.10%
0.00%
0.10%
0.20%
0 2 4 6 8 10 12 14 16 18 20
Baseline
No financial accelerator
-1.80%
-1.60%
-1.40%
-1.20%
-1.00%
-0.80%
-0.60%
-0.40%
-0.20%
0.00%
0 2 4 6 8 10 12 14 16 18 20
-0.15%
-0.10%
-0.05%
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0 2 4 6 8 10 12 14 16 18 20
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
0 2 4 6 8 10 12 14 16 18 20
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0 2 4 6 8 10 12 14 16 18 20
-0.10%
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
0.70%
0.80%
0 2 4 6 8 10 12 14 16 18 20
-0.20%
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
1.40%
0 2 4 6 8 10 12 14 16 18 20
-2.50%
-2.00%
-1.50%
-1.00%
-0.50%
0.00%
0.50%
0 2 4 6 8 10 12 14 16 18 20
-0.30%
-0.20%
-0.10%
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
0 2 4 6 8 10 12 14 16 18 20
-0.50%
-0.45%
-0.40%
-0.35%
-0.30%
-0.25%
-0.20%
-0.15%
-0.10%
-0.05%
0.00%
0 2 4 6 8 10 12 14 16 18 20
-5.00%
-4.50%
-4.00%
-3.50%
-3.00%
-2.50%
-2.00%
-1.50%
-1.00%
-0.50%
0.00%
0 2 4 6 8 10 12 14 16 18 20
-1.20%
-1.00%
-0.80%
-0.60%
-0.40%
-0.20%
0.00%
0.20%
0.40%
0.60%
0.80%
0 2 4 6 8 10 12 14 16 18 20
56
Figure 5. Turkey: The Monetary Transmission Mechanism
1
Source: Authors' calculations.
1
Interest rates, inflation rates, and the external finance premium are shown as absolute deviations from their
steady states, while the other variables are percentage deviations from their steady states.
-1.0%
-0.8%
-0.6%
-0.4%
-0.2%
0.0%
0.2%
0 2 4 6 8 10 12 14 16 18 20
Output Gap
-2.0%
-1.5%
-1.0%
-0.5%
0.0%
0.5%
0 2 4 6 8 10 12 14 16 18 20
Inflation rate (year-over-year)
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
0 2 4 6 8 10 12 14 16 18 20
Real exchange rate (increase indicates
0.0%
0.1%
0.1%
0.2%
0.2%
0.3%
0.3%
0.4%
0.4%
0.5%
0 2 4 6 8 10 12 14 16 18 20
External Finance Premium
-0.2%
0.0%
0.2%
0.4%
0.6%
0.8%
1.0%
1.2%
0 2 4 6 8 10 12 14 16 18 20
Real interest rate
-0.2%
-0.1%
0.0%
0.1%
0.2%
0.3%
0.4%
0.5%
0 2 4 6 8 10 12 14 16 18 20
Nominal interest
57
Figure 6. Historical Decomposition: The Role of Monetary Policy
(Demeaned year-over-year real GDP growth and shock contributions)
ations.
-25%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
-25%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
2005Q1 2005Q3 2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1
Other demand and supply shocks
Crisis shocks
Monetary policy
Growth (demeaned, real, year-over-year)
58
Figure 7. Historical Decomposition: Crisis Shocks
(Demeaned year-over-year real GDP growth and shock contributions)
-25%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
-25%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
2005Q1 2005Q3 2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1
Foreign demand
UIP
Financial uncertainty
Growth (demeaned, real, year-over-year)
59
Figure 8. Historical Decomposition: Other Supply and Demand Shocks
(Demeaned year-over-year real GDP growth and shock contributions)
-40.0%
-30.0%
-20.0%
-10.0%
0.0%
10.0%
20.0%
30.0%
-40.0%
-30.0%
-20.0%
-10.0%
0.0%
10.0%
20.0%
30.0%
2005Q1 2005Q3 2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1
Unit root technology
Other supply
investment
Other demand
Growth (demeaned, real, year-over-year)
60
Figure 9. Counterfactual Scenarios: The Role of Monetary Policy and Real GDP
(Levels)
.
60
65
70
75
80
85
90
95
100
105
60
65
70
75
80
85
90
95
100
105
2002Q1 2003Q1 2004Q1 2005Q1 2006Q1 2007Q1 2008Q1 2009Q1 2010Q1
Baseline
No monetary policy shocks
No response to output gap
Fixed exchange rate regime (peg)
Peg with heightened financial vulnerability
61
Appendix Table 1. Alternative Measuring of Actual and Simulated Recessions
1
.
1
See main text for details.
Cumulative
Growth Difference Difference
Baseline (actual) –4.8
No monetary policy shocks –5.9 –1.1 –1.1
No response to output gap –6.2 –0.3 –1.4
Fixed exchange rate regime (peg) –8.0 –1.8 –3.2
Peg with heightened financial vulnerability –9.3 –1.3 –4.5
Peak-to-trough
Cumulative
contraction Difference Difference
Baseline (actual) –14.4
No monetary policy shocks –15.4 –1.0 –1.0
No response to output gap –15.8 –0.4 –1.5
Fixed exchange rate regime (peg) –19.6 –3.8 –5.3
Peg with heightened financial vulnerability –22.1 –2.4 –7.7
Cumulative
Difference Difference
Baseline (actual)
No monetary policy shocks –5.7 –5.7
No response to output gap –1.5 –7.2
Fixed exchange rate regime (peg) –6.8 –14
Peg with heightened financial vulnerability –8.2 –22.2
(Relative to baseline)
In terms of peak-trough output contraction
(Percent)
(Percent of peak)
In terms of 2009 annual real GDP Growth
Annual average percentage loss
62
Appendix Figure 1a. Prior Posterior Distributions
(Parameters)
Calvo parameter (Domestic prices) Calvo parameter (Import prices) Calvo parameter (Wages) Indexation (Import prices)
Indexation (Domestic prices) Indexation (Wages) Monetary policy (Inflation response) Monetary policy (Output gap response)
Monetary policy (Exchange rate response) Monetary policy (Interest rate smoothing) Export demand elasticity Export demand inertia
Habit formation Investment adjustment cost
0
1
2
3
4
5
6
0 0.5 1
0
1
2
3
4
5
6
0 0.5 1
0
1
2
3
4
5
0 0.5 1
0
1
2
3
4
5
0 0.5 1
0
1
2
3
4
5
6
0 0.5 1
0
0 0.5 1
0
1
2
3
4
5
1 1.2 1.4 1.6 1.8 2
0
2
4
6
8
10
12
14
16
18
-0.1 0 0.1 0.2 0.3 0.4 0.5
0
1
2
3
4
5
6
7
8
9
10
-0.1 0.1 0.3 0.5
0
2
4
6
8
10
0 0.2 0.4 0.6 0.8 1
0
1
2
3
4
5
6
7
8
0 0.5 1 1.5
0
1
2
3
4
5
6
7
8
0 0.5 1
0
2
4
6
8
10
12
14
0 0.5 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 2 3 4 5 6
Posterior
Prior
63
Appendix Figure 1b. Prior Posterior Distributions
(Standard deviations of shocks)
Stationary technology Unit root technology Investment specific technology Domestic markup
Import markup Foreign inflation Foreign interest rate Country risk premium
Foreign demand Preference Labor supply Exogenous spending
Net worth Inflation target Monetary policy
0
100
200
300
400
500
0 0.02 0.04 0.06 0.08 0.1
0
20
40
60
80
100
120
0 0.02 0.04 0.06 0.08 0.1
0
50
100
150
200
0 0.02 0.04 0.06 0.08 0.1
0
20
40
60
0 0.02 0.04 0.06 0.08 0.1
0
1
2
3
4
5
6
0 0.5 1 1.5
0
20
40
60
80
100
0 0.02 0.04 0.06 0.08 0.1
0
100
200
300
400
0 0.02 0.04 0.06 0.08 0.1
0
10
20
30
40
50
60
70
0 0.02 0.04 0.06 0.08 0.1
0
10
20
30
40
50
60
0 0.05 0.1 0.15
0
200
400
600
800
1000
0 0.01 0.02 0.03 0.04 0.05
0
10
20
30
40
50
60
0 0.2 0.4 0.6 0.8
0
20
40
60
80
100
120
0 0.02 0.04 0.06 0.08 0.1
0
10
20
30
40
50
60
70
80
0 0.02 0.04 0.06 0.08 0.1
0
10
20
30
40
50
60
70
0 0.02 0.04 0.06 0.08 0.1
0
5
10
15
20
25
30
35
0 0.05 0.1 0.15 0.2
Posterior
Prior
64
Appendix Figure 1c. Prior Posterior Distributions
(Shock Processes Parameter)
Stationary technology Unit root technology Investment specific technology Domestic markup
Import markup Foreign inflation Foreign interest rate Country risk premium
Foreign demand Preference Labor supply Exogenous spending
Net worth Inflation target
0
1
2
3
4
5
6
0 0.5 1
0
1
2
3
4
5
0 0.5 1
0
1
2
3
4
5
0 0.5 1
0
1
2
3
4
5
0 0.5 1
0
2
4
6
8
10
12
0 0.5 1
0
4
8
12
16
20
0 0.5 1
0
1
2
3
4
5
0 0.5 1
0
1
2
3
4
5
0 0.5 1
0
1
2
3
4
5
6
7
0 0.2 0.4 0.6 0.8 1
0
5
10
15
20
25
30
0 0.5 1
0
1
2
3
4
5
6
7
8
9
0 0.5 1
0
1
2
3
4
5
6
7
0 0.5 1
0
1
2
3
4
5
0 0.5 1
0
1
2
3
4
5
0 0.5 1
Posterior
Prior
65
Appendix Figure 2a. Impulse Responses: UIP Shock
1
Appendix Figure 2b. Impulse Responses: Foreign Interest Rate Shock
1
Interest rates, inflation rates, and the external finance premium are shown as absolute deviations from their
steady states, while the other variables are percentage deviations from their steady states.
Output gap
Inflation (year-over-year) Nominal interest rate
Real exchange rate (increase indicates appreciation) External finance premium Real interest rate
-2.50%
-2.00%
-1.50%
-1.00%
-0.50%
0.00%
0 2 4 6 8 10 12 14 16 18 20
Baseline
Fixed exchange rate regime
No financial accelerator
-2.00%
-1.50%
-1.00%
-0.50%
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
0 2 4 6 8 10 12 14 16 18 20
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
0.70%
0.80%
0.90%
1.00%
0 2 4 6 8 10 12 14 16 18 20
-8.00%
-6.00%
-4.00%
-2.00%
0.00%
2.00%
4.00%
0 2 4 6 8 10 12 14 16 18 20
-0.20%
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
0 2 4 6 8 10 12 14 16 18 20
-0.20%
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
1.40%
1.60%
0 2 4 6 8 10 12 14 16 18 20
Output gap
Inflation (year-over-year) Nominal interest rate
Real exchange rate (increase indicates appreciation) External finance premium Real interest rate
-0.90%
-0.80%
-0.70%
-0.60%
-0.50%
-0.40%
-0.30%
-0.20%
-0.10%
0.00%
0.10%
0 2 4 6 8 10 12 14 16 18 20
Baseline
Fixed exchange rate regime
No financial accelerator
-0.60%
-0.40%
-0.20%
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
0 2 4 6 8 10 12 14 16 18 20
-0.05%
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0.35%
0.40%
0.45%
0 2 4 6 8 10 12 14 16 18 20
-3.00%
-2.50%
-2.00%
-1.50%
-1.00%
-0.50%
0.00%
0.50%
1.00%
0 2 4 6 8 10 12 14 16 18 20
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0.35%
0.40%
0 2 4 6 8 10 12 14 16 18 20
-0.10%
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
0.70%
0 2 4 6 8 10 12 14 16 18 20
66
Appendix Figure 3a. Impulse Responses: Foreign Demand Shock
1
Appendix Figure 3b. Impulse Reponses: Net Worth Shock
1
Interest rates, inflation rates, and the external finance premium are shown as absolute deviations from their steady states,
while the other variables are percentage deviations from their steady states.
Output gap
Inflation (year-over-year) Nominal interest rate
Real exchange rate (increase indicates appreciation) External finance premium Real interest rate
-4.50%
-4.00%
-3.50%
-3.00%
-2.50%
-2.00%
-1.50%
-1.00%
-0.50%
0.00%
0 2 4 6 8 10 12 14 16 18 20
Baseline
Fixed exchange rate regime
No financial accelerator
-5.00%
-4.00%
-3.00%
-2.00%
-1.00%
0.00%
1.00%
2.00%
0 2 4 6 8 10 12 14 16 18 20
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
0 2 4 6 8 10 12 14 16 18 20
-10.00%
-9.00%
-8.00%
-7.00%
-6.00%
-5.00%
-4.00%
-3.00%
-2.00%
-1.00%
0.00%
0 2 4 6 8 10 12 14 16 18 20
-0.20%
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
0 2 4 6 8 10 12 14 16 18 20
-0.20%
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
1.40%
1.60%
0 2 4 6 8 10 12 14 16 18 20
Output gap
Inflation (year-over-year) Nominal interest rate
Real exchange rate (increase indicates appreciation) External finance premium Real interest rate
-1.80%
-1.60%
-1.40%
-1.20%
-1.00%
-0.80%
-0.60%
-0.40%
-0.20%
0.00%
0.20%
0 2 4 6 8 10 12 14 16 18 20
Baseline
Fixed exchange rate regime
No financial accelerator
-4.00%
-3.00%
-2.00%
-1.00%
0.00%
1.00%
2.00%
3.00%
4.00%
0 2 4 6 8 10 12 14 16 18 20
-1.00%
-0.90%
-0.80%
-0.70%
-0.60%
-0.50%
-0.40%
-0.30%
-0.20%
-0.10%
0.00%
0 2 4 6 8 10 12 14 16 18 20
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
8.00%
9.00%
0 2 4 6 8 10 12 14 16 18 20
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
1.40%
1.60%
0 2 4 6 8 10 12 14 16 18 20
-1.20%
-1.00%
-0.80%
-0.60%
-0.40%
-0.20%
0.00%
0.20%
0 2 4 6 8 10 12 14 16 18 20
67
Appendix Figure 4a. Impulse Responses: Unit Root Technology Shock
Appendix Figure 4b. Impulse Reponses: Stationary Technology Shock
1
1
Interest rates, inflation rates, and the external finance premium are shown as absolute deviations from their steady states,
while the other variables are percentage deviations from their steady states.
Output gap
Inflation (year-over-year) Nominal interest rate
Real exchange rate (increase indicates appreciation) External finance premium Real interest rate
-12.00%
-10.00%
-8.00%
-6.00%
-4.00%
-2.00%
0.00%
0 2 4 6 8 10 12 14 16 18 20
Baseline
Fixed exchange rate regime
No financial accelerator
-4.00%
-3.00%
-2.00%
-1.00%
0.00%
1.00%
2.00%
0 2 4 6 8 10 12 14 16 18 20
-0.30%
-0.25%
-0.20%
-0.15%
-0.10%
-0.05%
0.00%
0.05%
0.10%
0.15%
0.20%
0 2 4 6 8 10 12 14 16 18 20
-7.00%
-6.00%
-5.00%
-4.00%
-3.00%
-2.00%
-1.00%
0.00%
0 2 4 6 8 10 12 14 16 18 20
-0.20%
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
0 2 4 6 8 10 12 14 16 18 20
-0.40%
-0.20%
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
1.40%
0 2 4 6 8 10 12 14 16 18 20
Output gap
Inflation (year-over-year) Nominal interest rate
Real exchange rate (increase indicates appreciation) External finance premium Real interest rate
-1.00%
-0.80%
-0.60%
-0.40%
-0.20%
0.00%
0.20%
0.40%
0 2 4 6 8 10 12 14 16 18 20
Baseline
Fixed exchange rate regime
No financial accelerator
-1.00%
-0.50%
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
0 2 4 6 8 10 12 14 16 18 20
-0.10%
-0.05%
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0 2 4 6 8 10 12 14 16 18 20
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
3.50%
0 2 4 6 8 10 12 14 16 18 20
-0.25%
-0.20%
-0.15%
-0.10%
-0.05%
0.00%
0.05%
0 2 4 6 8 10 12 14 16 18 20
-1.00%
-0.80%
-0.60%
-0.40%
-0.20%
0.00%
0.20%
0.40%
0 2 4 6 8 10 12 14 16 18 20
68
Appendix Figure 5a. Impulse Responses: Domestic Mark-up Shock
Appendix Figure 5b. Impulse Reponses: Foreign Inflation Shock
1
1
Interest rates, inflation rates, and the external finance premium are shown as absolute deviations from their steady states,
while the other variables are percentage deviations from their steady states.
Output gap
Inflation (year-over-year) Nominal interest rate
Real exchange rate (increase indicates appreciation) External finance premium Real interest rate
-0.60%
-0.50%
-0.40%
-0.30%
-0.20%
-0.10%
0.00%
0.10%
0 2 4 6 8 10 12 14 16 18 20
Baseline
Fixed exchange rate regime
No financial accelerator
-0.40%
-0.20%
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
1.40%
0 2 4 6 8 10 12 14 16 18 20
-0.04%
-0.02%
0.00%
0.02%
0.04%
0.06%
0.08%
0.10%
0.12%
0.14%
0 2 4 6 8 10 12 14 16 18 20
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
1.40%
1.60%
1.80%
0 2 4 6 8 10 12 14 16 18 20
-0.08%
-0.06%
-0.04%
-0.02%
0.00%
0.02%
0.04%
0.06%
0 2 4 6 8 10 12 14 16 18 20
-0.50%
-0.40%
-0.30%
-0.20%
-0.10%
0.00%
0.10%
0.20%
0 2 4 6 8 10 12 14 16 18 20
Output gap
Inflation (year-over-year) Nominal interest rate
Real exchange rate (increase indicates appreciation) External finance premium Real interest rate
-0.70%
-0.60%
-0.50%
-0.40%
-0.30%
-0.20%
-0.10%
0.00%
0.10%
0 2 4 6 8 10 12 14 16 18 20
Baseline
Fixed exchange rate regime
No financial accelerator
-1.40%
-1.20%
-1.00%
-0.80%
-0.60%
-0.40%
-0.20%
0.00%
0.20%
0.40%
0 2 4 6 8 10 12 14 16 18 20
-0.04%
-0.02%
0.00%
0.02%
0.04%
0.06%
0.08%
0.10%
0.12%
0.14%
0.16%
0 2 4 6 8 10 12 14 16 18 20
-1.50%
-1.00%
-0.50%
0.00%
0.50%
1.00%
0 2 4 6 8 10 12 14 16 18 20
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0 2 4 6 8 10 12 14 16 18 20
-0.05%
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0.35%
0.40%
0.45%
0 2 4 6 8 10 12 14 16 18 20
69
Appendix Figure 6a. Impulse Responses: Government Spending Shock
Appendix Figure 6b. Impulse Responses: Preference Shock
1
1
Interest rates, inflation rates, and the external finance premium are shown as absolute deviations from their steady states,
while the other variables are percentage deviations from their steady states.
Output gap
Inflation (year-over-year) Nominal interest rate
Real exchange rate (increase indicates appreciation) External finance premium Real interest rate
-6.00%
-5.00%
-4.00%
-3.00%
-2.00%
-1.00%
0.00%
0 2 4 6 8 10 12 14 16 18 20
Baseline
Fixed exchange rate regime
No financial accelerator
-2.00%
-1.50%
-1.00%
-0.50%
0.00%
0.50%
1.00%
0 2 4 6 8 10 12 14 16 18 20
-0.25%
-0.20%
-0.15%
-0.10%
-0.05%
0.00%
0.05%
0 2 4 6 8 10 12 14 16 18 20
-3.00%
-2.50%
-2.00%
-1.50%
-1.00%
-0.50%
0.00%
0.50%
0 2 4 6 8 10 12 14 16 18 20
0.00%
0.05%
0.10%
0.15%
0.20%
0.25%
0.30%
0.35%
0.40%
0.45%
0.50%
0 2 4 6 8 10 12 14 16 18 20
-0.20%
-0.10%
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
0 2 4 6 8 10 12 14 16 18 20
Output gap
Inflation (year-over-year) Nominal interest rate
Real exchange rate (increase indicates appreciation) External finance premium Real interest rate
-7.00%
-6.00%
-5.00%
-4.00%
-3.00%
-2.00%
-1.00%
0.00%
1.00%
2.00%
0 2 4 6 8 10 12 14 16 18 20
Baseline
Fixed exchange rate regime
No financial accelerator
-6.00%
-5.00%
-4.00%
-3.00%
-2.00%
-1.00%
0.00%
1.00%
2.00%
3.00%
4.00%
0 2 4 6 8 10 12 14 16 18 20
-1.60%
-1.40%
-1.20%
-1.00%
-0.80%
-0.60%
-0.40%
-0.20%
0.00%
0 2 4 6 8 10 12 14 16 18 20
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
8.00%
9.00%
10.00%
0 2 4 6 8 10 12 14 16 18 20
-0.80%
-0.60%
-0.40%
-0.20%
0.00%
0.20%
0.40%
0 2 4 6 8 10 12 14 16 18 20
-1.20%
-1.00%
-0.80%
-0.60%
-0.40%
-0.20%
0.00%
0.20%
0.40%
0.60%
0 2 4 6 8 10 12 14 16 18 20
70
Appendix Figure 7. Counterfactual Scenarios: Monetary Policy and Growth
(Year-over-year growth rates)
-35%
-30%
-25%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
-35%
-30%
-25%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
2002Q1 2003Q1 2004Q1 2005Q1 2006Q1 2007Q1 2008Q1 2009Q1 2010Q1
Baseline
No monetary policy shocks
No response to output gap
Fixed exchange rate regime (peg)
Peg with heightened financial vulnerability
71
REFERENCES
Adolfson, Malin, Stefan Laseen, Jesper Linde, Mattias Villani, 2007, ―Bayesian Estimation
of an Open Economy DSGE Model with Incomplete Pass-Through,‖ Journal of
International Economics, Vol. 72, pp. 481–511.
Adolfson, Malin, Stefan Laseen, Jesper Linde, Mattias Villani, 2008, ―Evaluating an
Estimated New Keynesian Small Open Economy Model,‖ Journal of Economic
Dynamics and Control, Vol. 32, pp. 2690–2721.
Aguilar, Mark and Gita Gopinath, 2007, ―Emerging Market Business Cycles: The Cycle is
the Trend,‖ Journal of Political Economy, Vol. 115. No. 1, pp. 69–102.
An, Sungbae and Frank Schorfheide, 2007, ―Bayesian Analysis of DSGE Models,‖
Econometric Reviews, Vol. 26, No. 2–4, pp. 113–72.
Blanchard, Olivier, Hamid Faruqee, and Mitali Das, forthcoming, ―The Initial Impact of the
Crisis on Emerging Market Countries,‖ Brookings Papers on Economic Activity.
Bloom, Nicholas, 2009, ―The Impact of Uncertainty Shocks,‖ Econometrica, Vol. 77, No. 3
(May), pp. 623–85.
Bernanke, Ben, and Mark Gertler, and Simon Gilchrist, 1999, ―The Financial Accelerator in
a Quantitative Business Cycle Framework,‖ Handbook of Macroeconomics, Vol. 1C,
pp. 1341–93 (Elsevier: Amsterdam).
Calvo, Guillermo, 1983, ―Staggered Prices in a Utility-Maximizing Framework,‖ Journal of
Monetary Economics, Vol. 12, pp. 383–98.
Calvo, Guillermo, Alejandro Izquierdo, and Luis. F. Mejia, 2004, ―On the Empirics of
Sudden Stops: The Relevance of Balance-Sheet Effects,‖ NBER Working Paper, No.
10520.
Cespedes, Luis F., Roberto Chang and Andres Velasco, 2004, ―Balance Sheets and Exchange
Rate Policy,‖ American Economic Review, Vol. 94, pp. 1183–1193.
Christiano, Lawrence J., Roberto Motto, and Massimo Rostagno, 2003, ―The Great
Depression and the Friedmand-Schwartz Hypothesis,‖ Journal of Money, Credit, and
Banking, Vol. 35, No. 6, pp. 1119–97
Christiano, Lawrence J., Roberto Motto, and Massimo Rostagno, 2008, ―Shocks, Structures,
or Monetary Policies? The Euro Area and U.S. After 2001,‖ Journal of Economic
Dynamics and Control, Vol. 32, pp. 2476–2506.
72
Christiano, Lawrence J., Roberto Motto, and Massimo Rostagno, 2010, ―Financial Factors in
Economic Fluctuations,‖ ECB Working Paper No. 1192.
Curdia, Vasco, 2007, ―Monetary Policy under Sudden Stops,‖ Federal Reserve Bank of New
York Staff Report No. 278.
Del Negro, Marco and Frank Schorfheide, 2008, ―Inflation Dynamics in a Small Open-
Economy Model Under Inflation Targeting: Some Evidence from Chile,‖ Federal
Reserve Bank of New York Staff Report No. 329.
Devereux, Michael B., and Charles Engel, 2002, ―Exchange rate pass-through, exchange rate
volatility, and exchange rate disconnect,‖ Journal of Monetary Economics, Vol. 49,
No. 5, pp. 913–40.
Dib, Ali and Ian Christensen, 2008, ―The Financial Accelerator in an Estimated New
Keynesian Model,‖ Review of Economic Dynamics, Vol. 11, pp. 155–78.
Duarte, Margarita, and Alan C. Stochman, 2005, ―Rational Speculation and Exchange
Rates,‖ Journal of Monetary Economics, Vol. 52, No. 1, pp. 3–29.
Eichengreen, Barry, and Ricardo Hausman, 1999, ―Exchange Rate and Financial Fragility,‖
NBER Working Paper No. 7418.
Elekdag, Selim, Alejandro Justiniano, and Ivan Tchakarov, 2006, ―An Estimated Small Open
Economy Model of the Financial Accelerator,‖ IMF Staff Papers, Vol. 53, pp. 219–41.
Elekdag, Selim and Ivan Tchakarov, 2007, ―Balance Sheets, Exchange Rate Policy, and
Welfare,‖ Journal of Economic Dynamics and Control, Vol. 31, pp. 3986–4015.
Gali, Jordi, and Tommaso Monacelli, 2005, ―Monetary Policy and Exchange Rate Volatility
in a Small Open Economy,‖ Review of Economic Studies, Vol. 72, No. 3, pp. 707–34.
Garcia-Cicco, Javier, 2010, ―Estimating Models for Monetary Policy Analysis in Emerging
Countries,‖ Central Bank of Chile Working Paper No. 561.
Gertler, Mark, Simon Gilchrist, Fabio Natalucci, 2007, ―External Constraints on Monetary
Policy and the Financial Accelerator,‖ Journal of Money, Credit and Banking, Vol. 39,
No. 2–3, pp. 295–330.
Ireland, Peter N., 2007, ―Changes in the Federal Reserve‘s Inflation Target: Causes and
Consequences,‖ Journal of Money, Credit, and Banking, Vol. 39, No. 8, pp. 1851–82.
Justiniano, Alejandro, Giorgio E. Primiceri, and Andrea Tambalotti, 2010, ―Investment
Shocks and Business Cycles,‖ Journal of Monetary Economics, Elsevier, Vol. 57, No. 2,
pp. 132–145, March.
73
Jeffreys, Harold, 1961, Theory of Probability, 3
rd
ed. (Oxford: Clarendon Press).
Kam, Timothy, Kirdan Lees, and Philip Liu, 2009, ―Uncovering the Hit-list for Small
Inflation Targeters: A Bayesian Structural Analysis,‖ Journal of Money, Credit and
Banking, Vol. 41, No. 4, pp. 583618.
Karagedikli, Ozer, Troy Matheson, Christie Smith, and Shaun P. Vahey, forthcoming, ―RBCs
and DSGEs: the computational approach to business cycle theory and evidence,‖ Journal
of Economic Surveys.
Kiyotaki, Nobuhiro, and John Moore, 1997, ―Credit Cycles,‖ Journal of Political Economy,
Vol. 105 (April), pp. 211–48.
Knight, Frank H., 1921, Risk, Uncertainty, and Profit. (Boston, MA: Hart, Schaffner &
Marx; Houghton Mifflin Company)
Lubik, Thomas A., and Frank Schorfheide, 2007, ―Do Central Banks Respond to Exchange
Rate Movements? A Structural Investigation,‖ Journal of Monetary Economics, Vol. 54,
pp. 1069–1087.
Mendoza, Enrique, 1991, ―Real Business Cycles in a Small Open Economy,‖ American
Economic Review, Vol. 81, pp. 7973–818.
Özatay, Fatih, 2009, Finansal Krizler ve Türkiye, (in Turkish), Doğan Kitap.
Ozkan, Gulcin, F., and D. Filiz Unsal, 2010, ―External Finance, Sudden Stops, and Financial
Crisis: What is Different This Time?‖ IMF Working Paper No. 10/158.
Schorfheide, Frank, 2000, ―Loss Function-Bases Evaluation of DSGE Models,‖ Journal of
Applied Econometrics, Vol. 15, pp. 1–48.
Smets, Frank and Rafael Wouters, 2003, ―An Estimated Stochastic General Equilibrium
Model of the Euro Area,‖ Journal of the European Economic Association, Vol. 1, pp.
1123–1175.
Smets, Frank and Rafael Wouters, 2007, ―Shocks and Frictions in US Business Cycles: A
Bayesian DSGE Approach,‖ American Economic Review, Vol. 97, No. 3. pp. 586–605.
Taylor, John B., 1993, ―Discretion versus Policy Rules in Practice,‖ Carnegie-Rochester
Conference Series on Public Policy, Vol. 39, pp. 195–214.
Taylor, John B., 1995, ―The Monetary Transmission Mechanism: An Empirical Framework,‖
Journal of Economic Perspectives, Vol. 9, No. 4, pp. 11–26.
74
Teo, Wing, 2009, ―Estimated Dynamic Stochastic General Equilibrium Model of the
Taiwanese Economy,‖ Pacific Economic Review, Vol. 14, No. 2, pp. 194–231.
Yalcin, Cihan and Mark Roland Thomas, 2010, ―External Imbalances Amplify the Crisis,
Domestic Strenghts Limit the Damage,‖ Chapter 11 in The Great Recession and
Developing Countries: Economic Impact and Growth Prospects, edited by Mustapha K.
Nabil, World Bank.
