Download Interest Rate Surprises and Transmission Mechanism in Turkey: Evidence from

WORKING PAPER NO: 15/04
Interest Rate Surprises and Transmission
Mechanism in Turkey: Evidence from
Impulse Response Analysis
January 2015
K. Azim ÖZDEMİR
© Central Bank of the Republic of Turkey 2015
Address:
Central Bank of the Republic of Turkey
Head Office
Research and Monetary Policy Department
İstiklal Caddesi No: 10
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The views expressed in this working paper are those of the
author(s) and do not necessarily represent the official views of the
Central Bank of the Republic of Turkey. The Working Paper Series
are externally refereed. The refereeing process is managed by the
Research and Monetary Policy Department.
Interest Rate Surprises and Transmission Mechanism in Turkey:
Evidence from Impulse Response Analysis1
K. Azim Özdemir
Abstract: This paper investigates the importance of interest rate shocks in explaining
macroeconomic dynamics during the relatively low-inflation period in Turkey after mid-2000s.
For this purpose, we compute impulse response functions using not only VAR models but also
multi-step ahead forecast regressions, which are referred as Local Projections. Estimations are
carried out on two different monthly data sets, a set of conventional series and a newly
constructed set of series for measuring real GDP, the price level and the exchange market
pressure in Turkey. Impulse responses obtained from newly constructed series exhibit more
plausible dynamics than the conventional series after an interest rate shock. Moreover, results
from Local Projections show remarkably similar dynamic responses to those obtained from the
VAR models. This finding can be interpreted as an evidence that the identified VAR models
successfully capture the true relationships among the variables.
Keywords: Monetary Policy, Identification, VAR, Local Projections, Interpolation
JEL Classification: C32, E52, C82
1
Address: The Central Bank of the Republic of Turkey, Istiklal Cad. No:10 06100-Ankara TURKEY, e-mail: [email protected]
I thank Kent P. Kimbrough and the referees for their helpful comments. This paper has been written while the author was visiting the
Department of Economics at Duke University, whose hospitality is gratefully acknowledged. Financial Support was provided by the Central Bank
of the Republic of Turkey. The views expressed here are not necessarily those of the Central Bank of the Republic of Turkey.
0
1- Introduction
Interest rate decisions by central banks, whether in the form of rate change or guidance, play a
crucial role in determining monetary policy stance and thereby the future course of economies.
However, empirical evidence does not consistently support the effectiveness of interest rate
decisions in attaining monetary policy goals. The objective of this paper is to contribute to this
discussion by exploring macroeconomic effects of short-term interest rates in Turkey. For this
purpose, we attempt to characterize the transmission mechanism of policy controlled interest
rate surprises using monthly series over the period 2004:1 to 2013:9, which corresponds to a
relatively low inflation phase of the Turkish economy.
This paper extends the analysis in the existing literature in two directions. First of all, this study
uses a new set of monthly data for analyzing monetary transmission mechanism in Turkey
whereas the previous studies typically employ monthly macroeconomic series such as the
industrial production index, the consumer price index and real or nominal exchange rates. We
believe that these conventional variables exclude a significant portion of the economic activity
and market forces. To fill this gap, this paper introduces economy-wide series namely, monthly
real GDP and the GDP deflator series, which are also benchmarked with the quarterly GDP and
GDP deflator series released by the Turkish Statistical Institution. For the exchange rate
movements, on the other hand, we calculate an index of exchange market pressure, which is
designed to account for the FX market interventions by central banks together with the market
forces. Secondly, the robustness of standard moving average computation of impulse response
functions obtained from VAR models are examined by running multi-step forecast regressions,
which are referred as the local projections by Jorda (2005).
Results from the impulse response functions suggest that the VAR models, which are estimated
by using the conventional series, are only partly able to explain macroeconomic responses to
the interest rate surprises. In fact, the dynamic responses to the interest rate shocks are more
plausible when we use real GDP, the GDP deflator and the Exchange Market Pressure index in
the estimation of the VAR model. The peak response of the GDP deflator is observed in the
months of the fourth quarter after the shock. Moreover, the responses of real GDP, real private
credit and FX market are much faster than the response of the GDP deflator as expected. The
effects on these variables reach a peak at the end of the first quarter. Using the conventional
series, however, we observe that the response of the consumer price index is much faster than
the real economic activity, which contradicts with the predictions of the existing economic
theories. Furthermore, the real exchange rate tends to depreciate after a surprise increase in
the short-term interest rate. This finding suggests that the exchange rate puzzle might continue
to be a problem in the low inflation period if the real exchange rate was considered to be the
right measure of the exchange market pressure in Turkey. Finally, results from local projections
produce remarkably similar dynamic responses to those obtained from the estimated VAR
models. This finding can be interpreted as an evidence that the identified VAR models are good
approximations to the true relationships among the variables.
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The rest of the paper is structured as follows: Section 2 discusses some of the related literature
about the transmission of interest rate shocks. In Section 3 we describe the data sets and
provide a brief review of the literature related to the interpolation of high frequency series
from indicator variables. In this section we also discuss the Exchange Market Pressure index and
its relevance to our context. Section 4 presents the methodological issues for estimating and
identifying VAR models adopted in this paper. This section also illustrates the methodologies
for deriving the impulse response functions. The empirical results are provided in Section 5.
Finally, Section 6 concludes.
2- Related Literature
An innovation to short-term interest rate is generally accepted as an exogenous variation in
monetary policy. Bernanke and Mihov (1998) derives monetary policy innovations by modeling
market for bank reserves as part of a general macroeconomic model estimated by using semistructural VAR identification scheme. They present evidence that federal funds rate is a good
indicator of monetary policy, except for a brief period at the beginning of 1980s during which
the FED targeted non-borrowed reserves of the banking system. Their results also confirm
earlier findings reported in Bernanke and Blinder (1992). Since then many research papers have
used interest rate as the main policy instrument in monetary VAR models. In this paper we also
use a short-term interest rate to quantify surprise changes in the Turkish monetary policy.
An informal test of an identified VAR model is to check whether the estimated impulse
response functions are in accordance with the view generally held of the macroeconomic
impact of monetary policy impulses. This is the approach we also adopt in this paper. It is
widely accepted that monetary contraction should lead to a faster appreciation of domestic
currency, temporary output losses and a sluggish but lasting decrease in price level (Christiano
et al., 1999). Contrary to this widely accepted view, many studies report positive and protracted
response of price level and depreciation of domestic currency known as the “price puzzle” and
“exchange rate puzzle”. Some economists link these anomalous responses to missing variables
in VAR models while others relate them to the misidentification of monetary policy shocks by
questioning the standard identification schemes.
Following Sims (1992) and Bernanke and Mihov (1998), commodity price index has been used
as a missing information variable in monetary VAR models in order to address the “price
puzzle”. They show that this problem is caused by central banks’ reaction to forecasted inflation
and can be solved by including commodity prices. However, Hanson (2004) stresses on the role
of misidentified monetary policy shocks and argues that the gain of inclusion of an index of
commodity prices or any other information variable can vary in importance over different
sample periods for the US case. As a result he concludes that the usefulness of information
variables used in place of a missing variable in monetary VAR models cannot be established
empirically across all sample period. In a similar vein Barakchian and Cowe (2013) present some
evidence on the changing nature of monetary policy and the importance of correctly identifying
monetary policy shocks after 1980s and its impact on the “price puzzle”. While there are some
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merits to this latter argument, we prefer to draw on the literature on the role of missing
variables in VAR models in this paper. Following the common practice of the literature, the data
set used for estimating our models also include commodity price index and series that reflect
the developments in the global economy.
Another prevalent problem, especially for small open economies, is the “exchange rate puzzle”
which indicates a counter intuitive response to unexpected interest rate shocks. In contrast to
the prediction of the theories as postulated under the interest parity conditions, the empirical
research commonly present results that a tight monetary policy initially leads to a significant
depreciation of the domestic currency. Sims (1992), Grilli and Roubini (1995) provide extensive
search on this topic for developed economies. Most of the countries included in their studies
produce exchange rate depreciation after positive interest rate innovations. Given the
extensive documentations on the “exchange rate puzzle”, Cushman and Zha (1997) argue that
there is either a need for a new theory, which can account for this kind of exchange rate
responses or monetary policy has not been successfully identified in open economies.
Consequently they propose a new identification scheme that can differentiate the policy
reaction function from private sector responses to both policy actions and changes in foreign
variables. This paper follows a different path and argues that ignoring restrictions on the free
determination of exchange rates, such as central bank interventions, might be responsible for
this kind of anomalous response of exchange rates to interest rate shocks, especially for
countries where their central banks use FX market interventions under a free exchange rate
regime. To address this issue we construct an Exchange Market Pressure Index, which is the
weighted sum of central banks’ foreign assets and the nominal exchange rates, rather than
directly using the real or nominal exchange rates in the estimations.
The transmission of monetary policy innovations are generally analyzed by computing impulse
response functions. The traditional impulse response functions, which are derived by
transforming a VAR model into a moving average representation, provide forecasts for
increasingly distant future periods. Therefore, if an estimated VAR model is not a good
approximation of the true data generating process (DGP), the forecast performance is not
expected to be satisfactory at multi-period horizons. As a result, the computed impulse
responses cannot be accurate either. Jorda (2005) proposes an alternative method for deriving
impulse responses, which he refers as local projections. He argues that local projections are
robust to misspecifications and solve the problems with the traditional approach. Haug and
Smith (2007) apply this method to the New Zealand economy and finds that the dynamic
responses to monetary policy shocks are much more plausible when local projections are used
for computing the impulse response functions. By using a structural VAR IS-LM model, Ronayne
(2011) shows that the bias from traditional structural impulse responses becomes more severe
at horizons higher than the lag length. He concludes that local projections offer a remedy to this
problem and should be a part of studies on macroeconomic dynamics. Reflecting the growing
interest on local projections this paper also presents results by applying Jorda’s method to
compute impulse response functions. Since a correctly specified VAR model is expected to
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produce a similar forecast performance with multi-step direct forecast equations, the
computed impulse response functions from these two methods should exhibit similar dynamic
patterns. Therefore, this method also allows us to examine the robustness of the impulse
response functions, which is carried out in the empirical part of the paper.
The evolution of the transmission mechanism and the puzzling dynamics have been also
documented in a number of studies for the Turkish economy. Çatık and Martin (2012) discuss
the behavior of monetary transmission mechanism in Turkey by dividing the data set from the
mid-2000s into two periods, since this is the time when low inflation and interest rate regimes
come into play. Their results suggest that while the price puzzle after 2004 is less evident, the
exchange rate puzzle continues to exist in the low inflation regime albeit at a lesser extent.
Although their identification of regime changes are based on a rigorous analysis of the data set,
the number of observations remained in the low inflation period were not enough to use
quarterly economy-wide series, such as real GDP and the GDP deflator. Therefore, their
monthly analysis of the monetary policy transmission mechanism is subject to limitations of
using less comprehensive series such as the industrial production and consumer price indices.
By constructing monthly real GDP and the GDP deflator series, this paper extends their analysis
to the economy-wide responses.
The ambiguity about the correct measures of monetary policy in Turkey has led some
researchers to investigate alternative identifications of monetary policy shocks. In a series of
papers Berument (2007), Berument et al. (2011) and Berument et al. (2014) consider relative
movements of short-term interest rates with respect to the FX and bond markets. Before the
crisis in 2001, Berument (2007) argue that the spread between interbank rates and the monthly
depreciation rates of the Turkish Lira captures the policy practice of the CBRT. After the crisis,
however, they construct a new liquidity variable which take into account the Central Bank
operations both through TL and FX markets (see Berument et al., 2011). In a recent paper
Berument et al. (2014) extend the set of policy measures by including the spread between
interbank rate and a weighted average of the monthly treasury auction rates. They argue that
this new policy measure is immune to the changes in the CBRT’s policy framework after 2010.
The results of the papers obtained from VAR models are in line with their expectation and the
prediction of the main stream theories. They show that there are an appreciation for the
Turkish Lira and negative but sluggish response of the prices after a tight monetary policy
innovation.
On the other hand, Kılınç and Tunç (2014) employ a structural VAR approach in order to identify
monetary policy shocks in Turkey. As in Çatık and Martin (2012) their model includes monthly
series for both domestic and foreign variables over the most recent low inflation period in
Turkey, 2006m1-2013m7. The impulse response functions obtained from their identified VAR
model show no indication of puzzling dynamics for the price and exchange rate variables after a
tight monetary policy shock. Their model also produces an immediate and simultaneous decline
in the prices and real economic activity. In contrast to the recursive identification scheme used
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in Çatık and Martin (2012) their identification structure seems to be more rigorous, but it
excludes the contemporaneous response of monetary policy to shocks arising from the real
sector, which might be important during the inflation targeting period. By using a semistructural VAR approach we aim at using a more comprehensive identification scheme than the
one used in Kılınç and Tunç and Berument et al. (2014). As a result our identification strategy
allows monetary policy to react endogenously to any shocks in the model without any
restrictions.
Erdoğan and Yıldırım (2010) analyze the interest rate channel of monetary policies before and
after the financial crisis in 2001 by using monthly series. They find that the output response to
an interest rate surprise becomes stronger in the post crisis period in Turkey. However, in
contrast to the results reported by in the studies mentioned in the previous paragraphs their
results subject to the “price puzzle” which is explained by the rising production cost. This is
probably caused by not correctly identifying the regime switch since the high inflation rates
continued to shape the economic environment for a considerable period of time after the crisis
in 2001. In fact, the impact of high inflation environment on the economic relations in Turkey
are analyzed in a number of papers. For example, Aktaş et al. (2010) argue that the “price
puzzle” in the pre-inflation targeting regime reflect the structural characteristic of the Turkish
economy in that time. As a result they conclude that the puzzling dynamics in the high inflation
period are the result of the interaction between monetary contraction and the increasing risk
premium. However, these studies do not attempt to solve the “exchange rate puzzle” and
implicitly assume that this phenomenon is the results of the structural characteristics of the
Turkish economy. With this paper we explicitly address this issue by constructing an Exchange
Market Pressure Index, which accounts for the central bank interventions as well as the market
forces in the determination of exchange rates.
Başcı et al. (2007) provide further evidence on the changing structure of the monetary
transmission channel after the 2001 crisis in Turkey. Their paper underlines the increasing role
of the interest and credit channels during this period. By using the same period Aktaş et al.
(2008) focus on the response of the financial markets to an unexpected change in the policy
controlled interest rates. Their results emphasis the significant role of the bonds and bills
market in the transmission of an unexpected monetary policy shock. With a broader
perspective, Akyürek et al. (2011) present results that confirm the success of macroeconomic
stabilization through monetary policy in the low inflation period after 2002. On the other hand,
Demiralp (2008) analyzes the role of bank deposits in the transmission of monetary policy and
its impact on the loan supply in Turkey. The results of the paper support the credit view of the
monetary transmission channel and it proposes monetary control through using reserve
requirement as monetary policy instrument. However, these studies are silent about the
dynamic responses of the important macroeconomic variables to interest rate shocks, which
are the main focus of our paper.
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3- Data
In this paper we use monthly observations that covers the low-inflation regime in Turkey. The
shift of the Turkish economy from the chronic high inflation environment to the low-inflation is
identified in Catik and Martin (2012) by employing a Threshold-VAR model. Using interest rate
as the threshold variable, they find evidence that the transition happened in March 2004. With
the inflation rate as the threshold variable, the transition date is identified as of July 2003. By
following their results we choose 2004:1 as the start of our sample period, which is in between
these two transition dates. Since our sample period ends in 2013:9, it also covers the inflationtargeting regime and the new monetary policy framework implemented after the global
financial crisis in the late 2008.
GDP and the GDP deflator are only available at quarterly frequencies, however using the
monthly data may have a number of advantages in our context. First of all, quarterly
observations are not sufficient to estimate reliable coefficients for the low-inflation regime.
Secondly, the recursive identification assumptions generally used in analyzing monetary
transmission mechanism can be better defended if monthly or higher frequency data are used
in VAR models since these identification schemes generally assume no feedback from policy
surprises to the economy. While these arguments favor using monthly data, it is not always
feasible in practice as the economy wide real activity and price movements may not be
available at monthly frequencies.
One of the solution in empirical works is to use monthly series of industrial production and
consumer price indices in order to approximate real economic activity and price developments.
This practice is criticized by some authors (Kilian, 2011) as they think these monthly series can
account for only a fraction of the real activity and price movements. Another solution proposed
in the literature is to interpolate these variables by using monthly announced components of
the national income accounts or by utilizing variables that may contain information about them
and at the same time available at higher frequencies. In this paper we use both approaches in
order to check the consistency of the results. With this strategy it will be also possible to
uncover the similarities and differences and to understand the appropriateness of the
conventional series in analyzing the monetary transmission mechanism.
In the literature there exists several techniques for estimating a high frequency (monthly or
quarterly) series from an observable low frequency (quarterly or annual) series by using high
frequency indicator variables. These techniques are summarized and compared in Chen (2007)
under different assumptions about the availability of the series, the type of the constraints and
the values (negative or positive values) of the observable low frequency series. After evaluating
60 series from the National Income Accounts, he concludes that Denton Proportional First
Difference Method outperforms all other methods according to five specific criterions he uses
to ensure high quality in order to be used by the statistical agencies.
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Following Chen, in this paper we also use the Denton Proportional First Difference Method (see
Denton, 1971) to interpolate monthly data series for the real economic activity and price
developments. In Denton method, the estimated high frequency unobservable series, which is
monthly in our case, are derived by minimizing an objective function subject to the binding
restrictions which impose the condition that the sum of the estimated monthly series over the
quarter has to be equal to the observable quarterly data. In the proportional version of the
Denton method, the minimized series is the ratio of the estimated high frequency data to the
indicator variable available at that frequency (see IMF 2001). We can express the minimization
problem of the proportional Denton Method as follows;
𝑇
𝑋𝑡 𝑋𝑡−1 2
⏟∑( −
𝑚𝑖𝑛
)
𝐼𝑡
𝐼𝑡−1
𝑋
(1)
𝑡=2
𝑡 = 1, … 3𝑀 … , 𝑇
Subject to the binding restriction;
𝑇
∑ 𝑋𝑡 = 𝑍𝑞𝑦
(2)
𝑡=2
𝑞𝑦 = 1, … . 𝑀
Where 𝑡 is time, 𝑋𝑡 is the estimated monthly series for month 𝑡, 𝐼𝑡 is the indicator variable for
month 𝑡, 𝑀 is the quarter of the year for which quarterly values are available (eg. 2013q3), 𝑞𝑦
is the quarter of the year, 𝑍𝑞𝑦 is the quarterly value, 𝑇 is the last month for which monthly data
for the indicator variable is available. In the above expression, the goal of the method is to keep
the ratio of the estimated monthly series to the indicator series as constant as possible given
the restriction. In accordance with this goal, the Proportional Denton Method transmits the
short-term movement in the indicator variable as much as possible to the estimated variable by
observing consistency with the benchmark or the reliable low frequency series.
Table 1 shows the interpolated variables and indicators used in the calculation of monthly GDP
for the Turkish economy. This table also presents the correlation coefficients between the
observable quarterly variables and the indicator variables. When we consider the level of the
variables the correlation coefficients, except for the stock changes, reveal strong correlations
between observable variables and the selected indicator variables. These correlations are in the
range of 0.84 and 0.99. In the case of the stock changes it is difficult to find an indicator
variable, since this component of the GDP also includes accounting errors in the national
income accounts. The last column of Table 1 shows the correlation coefficients when we
consider the quarterly differences of the variables. Although there seems to be some decreases
in the correlation coefficients for the case of the public sector deflator and the construction
investment of the private sector, they remain high for the rest of the variables.
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The first set of interpolated monthly variables in Table 1 are for the economy wide price
movements, which can be approximated by the GDP deflator, and the public sector pricing
behavior. Interpolation for the GDP deflator is performed by finding the first principal
component of the consumer price index, producer price index and domestic producer price
index and using this as the indicator variable. For the public sector deflator, the public sector
subcomponent of the whole sale price index is used for the period before 2005. After 2005, this
subcomponent is not available anymore. Therefore, we use the producer price index with the
highest correlations at the quarterly frequency among other available price indices to
approximate the pricing behavior of the government sector.
After estimating the monthly deflators, we turn to estimating the nominal items of the public
sector in the National Income Account. These items are announced through the Central
Government Budget, which accounts for almost 90 % of the general public sector, at monthly
frequencies. We proceed in two steps to interpolate the public sector items in the National
Income Accounts. First we use the corresponding items of the budget in the interpolation of the
nominal personnel expenditures, purchases of goods and services and total investment of the
public sector. Then we deflate the estimated nominal figures by using the public sector deflator
to obtain the real values.
The rest of the item in the national income accounts are directly interpolated by using real
indicator variables. We start with the real private consumption, which has the largest share in
the national income accounts. For this item, a few candidates are available to be used as an
indicator variable. These are the consumer confidence index, its sub-items and tax revenues on
domestically sold goods and services. However, the highest correlation exists between the real
private consumption and the real tax revenues at the quarterly frequency. Therefore, we
employ this variable in the estimation. In the case of real private investment, the components
of the industrial production index, machinery investment and metal production other than
machinery investments, are used in the interpolation. For the foreign sector, the export and
import quantity indices show strong correlations with import and export components of the
National Income Account as expected. Therefore, we use these indices as the indicator
variables for the real export and imports. Finally, we need to estimate monthly stock changes to
sum up to the monthly real GDP. For estimating stock changes the question “Current Stock
Level Compared to Normal” of the Economic Tendency Survey is used for the period after
2007:1. Before this period we extrapolate monthly seasonal pattern of the survey and use this
new variable as the indicator variable in the interpolations of the stock changes.
In a small open economy, another complication with the data set used in the monetary
transmission analyses is the difficulty of including a right measure of exchange market
movements. While many of these countries have adopted floating exchange rate regimes, it is
not an uncommon practice of their central banks to limit exchange rate flexibility through
exchange market interventions. Therefore, it is argued that when the objective is to measure
the pressure in the FX market, the researcher should not only focus on exchange rates but also
8
take into account movements of foreign assets of the central bank. In the literature there are a
number of approaches that account for those movements jointly. Several studies (Girton and
Roper, 1977; Eichengreen et al., 1996; Tanner, 2001) have proposed constructing indices based
on the summation of the growth rates of the exchange rate and a normalized ratio of the
foreign assets of the central banks. This kind of indices are generally known as the Exchange
Market Pressure Index (EMPI).
In this paper we also construct an EMP index following the methodology as in Tanner (2001).
Tanner derives EMP index from an equilibrium condition between money supply and money
demand by using a simplifying assumption of one to one relation between inflation and
nominal exchange rate after adjusting for the world inflation. His final equation takes into
account both the growth rate of the nominal exchange rates and the normalized changes in
international reserves. Therefore, the calculated EMP index corrects the biases in the nominal
exchange rate by adjusting the effect of the sterilization policy. It can be expressed as follows;
𝑒𝑡 − 𝑒𝑡−1
𝑛𝑓𝑎𝑡 − 𝑛𝑓𝑎𝑡−1
𝐸𝑀𝑃𝑡 = (
)−(
)
𝑒𝑡
𝑏𝑚𝑡−1
(3)
Where 𝑒𝑡 is the exchange rate, 𝑛𝑓𝑎𝑡 is the net foreign assets of the central bank and 𝑏𝑚𝑡 is the
base money. In the Turkish case 𝑒 stands for the value of TL per unit of a basket that consists of
1 US Dollar+1 Euro and 𝑛𝑓𝑎 is obtained by subtracting foreign liabilities owed to nonresidents
from the foreign assets of the central bank. Our calculation does not subtract the banking
sector’s foreign deposits held at the central bank from the foreign assets. Each expressions in
the brackets are standardized and therefore they are assumed to have zero mean and unit
variance.
The rest of the domestic variables used in our VAR model are the volume of real credit
extended to the private sector and a short-term interest rate. For the short-term interest rate
we use the repo rate between 7 to 14 days maturity in the market organized under the Istanbul
Stock Exchange. The main reason for preferring this rate is that it is capable of reflecting the
effect of liquidity policy of the Central Bank. Furthermore, it is also closely related to the
overnight repo rates, which are mainly guided by the policy targets of the Central Bank of
Turkey. In order to compare the results with the earlier literature, our data set also includes
widely used variables in the literature such as industrial production index, consumer price index
and real exchange rate as alternatives to monthly GDP, the GDP deflator and Exchange Market
pressure Index constructed in this paper. The complete set of domestic variables are illustrated
in Figure-1.
In addition to the domestic variables, VAR models estimated in this paper assume an exogenous
set of foreign variables that may affect the dynamic of monetary policy shocks. These foreign
variables are contemporaneous and lagged values of monthly GDP and a short term interest
9
rate for the US economy and the Moody’s Commodity Price Index2. It is argued that the
inclusion of these variables may alleviate the puzzling dynamics after a monetary policy shock.
It is also possible that these variable may capture the effect of the Global Financial Crisis in
2008 and in the aftermath the expansionary monetary policies of the developed economies
since the origin of the crisis and the leading role of the monetary policy response can be traced
to the U.S markets and the Federal Reserve. By assuming exogeneity for these variables, we
consider no feedback from domestic variables to the foreign sector, which can be taken as a
realistic assumption given the size and importance of the Turkish economy for the rest of the
world.
Finally, each of the VAR-models is estimated in levels rather than in difference form. Sims et al.
(1990) advise using levels even in the presence of possible unit roots and cointegration among
the variables. They argue that the consistent estimates of the parameters can be obtained by
applying ordinary least squares to levels. The data used in the estimation are in the log form
and seasonally adjusted, except the interest rates.
4- Methodology
The main focus of this paper is to characterize macroeconomic responses of the Turkish
economy to a policy guided interest rate shock by computing impulse response functions. For
this purpose we estimate VAR models and multi-step direct forecast regressions. In the VAR
models we employ a semi-structural approach for identifying exogenous monetary policy
shocks. Accordingly, we use a recursive identification scheme by placing policy variables at the
end of the ordering. Therefore, policy shocks have no contemporaneous impact on the
macroeconomic variables included our models.
Our general monetary VAR model consists of a vector of 5 variables 𝑧𝑡 = [𝑝𝑡 𝑦𝑡 𝑐𝑡 𝑒𝑡 𝑖𝑡 ] where
𝑝𝑡 denotes price level, 𝑦𝑡 real economic activity, 𝑐𝑡 private credit, 𝑒𝑡 exchange rate and 𝑖𝑡 shortterm interest rates. They are estimated by using two sets of data as explained in the previous
section. In the first VAR model, price level, real economic activity and exchange rate are
interpreted as the series interpolated in the previous section, namely the monthly GDP
deflator, monthly real GDP, and the exchange market pressure index respectively. In the second
VAR model, we replace these variables with the consumer price index, the industrial production
index and the real exchange rate. We also use the same ordering as in 𝑧𝑡 vector which places
policy related variables, interest rate and exchange rate, at the end of the ordering. Since our
concentration is on the interest rate surprises we prefer to place it last rather than the
exchange rate.
2
The monthly GDP series for the US economy are taken from Macroeconomic Advisers’ web page. The rest of the
exogenous variables are from the GFD database.
10
We consider the following representation of the reduced form VAR model3;
Φ(𝐿)𝑧𝑡 = 𝑢𝑡
(4)
Where 𝑧𝑡 is for a vector of 𝑘 dimensional variables and Φ(𝐿) ≡ 𝐼 − Φ1 𝐿 − Φ2 𝐿2 −
⋯ … … . −Φ𝑝 𝐿𝑝 the autoregressive lag order polynomial of the coefficient matrices Φ𝑖 . In the
latter expression 𝐼 denotes identity matrix and 𝐿 lag operator. 𝑢𝑡 represents the white noise
disturbances with the covariance matrix 𝐸(𝑢𝑡 𝑢𝑠′ ) = Σ if 𝑡 = 𝑠 and 0 otherwise. Identification
requires finding a lower triangular matrix 𝐴 that converts the disturbance vector to a vector of
orthogonal structural innovations, 𝐴𝑢𝑡 = Λ𝜀𝑡 . This can be achieved by solving 𝑘 equations of
the following system,
′
𝐴−1 ΛΛ′ 𝐴−1 = Σ
(5)
where Λ is the diagonal matrix with elements, 𝜎𝑖,𝑡 and 𝜀𝑡 normalized errors with 𝜀𝑡 ∼ 𝑁(0, 𝐼).
Since Σ is a symmetric matrix, the necessary condition for the solution of the simultaneous
relationships requires(𝑘 2 − 𝑘)⁄2 restrictions in the system for the exact identification.
The analyses of the transmission of interest rate shocks are generally based on the impulse
response functions obtained from the VAR models. The impulse response functions are simply
the standard moving average representations of the variables in the estimated VAR models.
However, the evidence presented in several studies ( e.g. Kapetanios, Pagan and Scott. 2007;
Lippi and Reichlin, 1993; Phillips, 1998; Pesavento and Rossi, 2007) suggest that impulse
responses derived from the vector moving average (MA) representation of a VAR model have
serious consistency and reliability problems. These problems mainly result from missing
variables and dynamics, the presence of unit root and cointegration among variables in the VAR
system. In order to overcome these problems, Jorda (2005) proposes a multi-step projection
approach, which he refers as local projections. He demonstrates that impulse responses
derived from the local projection technique has desirable properties over moving average
representation in terms of the consistency. In addition to the Vector-MA approach we also use
his approach to check the robustness of the standard moving average representations.
We can rewrite the reduced form VAR in order to formalize the derivation of the impulse
response functions (IRF),
𝑧𝑡 = Φ1 𝑧𝑡−1 + Φ2 𝑧𝑡−2 + ⋯ . +Φ𝑝 𝑧𝑡−𝑝 + 𝐴−1 𝜀𝑡
(6)
and consider two series of shocks
𝜀𝑖𝑡 = 𝜀̅𝑖𝑡 𝑎𝑛𝑑 𝜀𝑖𝑡 = 𝜀̃𝑖𝑡 = {
3
𝜀̅𝑖𝑡 + 𝜎𝑖𝜏
𝜀̅𝑖𝑡
𝑖𝑓 𝑡 = 𝜏
𝑜. 𝑤
(7)
For simplicity we do not include intercept terms. However, in the estimation we use intercept terms.
11
where 𝜎𝑖𝑡 is the standard deviation of shock series 𝜀̅𝑖𝑡 and 𝑖 = 1,2, … . . , , 𝑘. The IRF measures
the response of the system’s variables in equation (6) if ith variable in the system is shocked one
standard deviation at time 𝑡 = 𝜏. Then the IRF can be expressed as follows,
𝐼𝑅𝐹(𝑡, ℎ, 𝜀𝑖 ) = 𝐸(𝑧𝑡+ℎ |𝜀̃𝑖,𝑡+ℎ ; Ω𝑡 ) − 𝐸(𝑧𝑡+ℎ |𝜀̅𝑖,𝑡+ℎ ; Ω𝑡 )
(8)
where ℎ = 0,1, … , 𝐻 is the horizon at which IRFs are calculated as the updated forecast after
disturbing the system by one standard deviation. In fact the IRF function is independent from
the realization of 𝜀𝑖𝑡 series. Therefore, we can assume 𝜀̅𝑖𝑡 = {0} and 𝜎𝑖𝑡 to be any magnitude of
disturbance vector. Ω𝑡 summarizes the information available in the lagged and exogenous
variables when a shock hits the system at time 𝜏.
The conventional way to obtain the IRF is to solve the dynamic equations in (6) once the VAR
model is estimated to recover the true data generating process (DGP). The solution transforms
the vector autoregressive process into a sum of deterministic and stochastic parts. This
solution, which is referred as the moving average (MA) representation or the Wold’s
decomposition, nests the elements of the impulse response functions as the sets of coefficients
in the stochastic part. The solution can be written as fallows,
∞
𝑧𝑡 = 𝑧̅ + ∑ 𝜑ℎ 𝜀𝑡−ℎ
(9)
ℎ=0
where 𝜑 is 𝑘 × 𝑘 coefficients matrix. Each set of elements of the matrices 𝜑ℎ for ℎ =
0,1, … , 𝐻, … , ∞ constitute the impulse response functions given in equation (9).
As mentioned in the previous paragraphs, an alternative approach to compute the impulse
response function is to employ local projections that do not require any knowledge about the
true DGP. Jorda (2005) defines local projections as the collection of h steps ahead forecast
equations,
ℎ
𝑧𝑡+ℎ = 𝛼 ℎ + 𝑃1ℎ+1 𝑧𝑡−1 + ⋯ … … + 𝑃𝑝ℎ+1 𝑧𝑡−𝑝 + 𝜀𝑡+ℎ
(10)
where 𝛼 is a 𝑘 × 1 vector of constants, 𝑃 coefficient matrices of lagged variables and
ℎ = 0,1,2, … . , 𝐻 − 1. Using the expression in (7) and local projections in equation (10) we can
write the impulse response functions from local projections as follows,
𝐿𝑃𝐼𝑅𝐹(𝑡, ℎ, 𝜎𝑖 ) = 𝑃̂1ℎ 𝜎𝑖
(11)
The LPIRF coincides with the responses obtained from the VAR model for ℎ = 0 since the
system reduces to a VAR. For ℎ = 1, … , 𝐻 − 1, 𝑃̂1ℎ denotes the matrix that contains the
estimated impulse response coefficients. In fact it is the slope coefficients for the vector 𝑧𝑡 ,
which is estimated by substituting its forecasted value. This can be shown by using equation (9)
and (7),
𝐿𝑃𝐼𝑅𝐹(𝑡, ℎ + 1, 𝜎𝑖 ) = 𝐸[𝛼 ℎ+1 + 𝑃̂1ℎ+1 (𝛼 0 + 𝑃̂10 𝑧𝑡−ℎ−1 + ⋯ . +𝑃̂𝑝0 𝑧𝑡−ℎ−𝑝 + 𝜎𝑖 ) + ⋯ + 𝑃̂𝑝ℎ+1 ]
12
(15)
−𝐸[𝛼 ℎ+1 + 𝑃̂1ℎ+1 (𝛼 0 + 𝑃̂10 𝑧𝑡−ℎ−1 + ⋯ . +𝑃̂𝑝0 𝑧𝑡−ℎ−𝑝 + 0) + ⋯ + 𝑃̂𝑝ℎ+1 ]
Jorda (2005) shows that impulse responses computed by estimating local projections are
consistent. However, he notes the loss of efficiency which may arise from excluding the moving
average structure. As a solution he suggests either performing the regressions with a
heteroskedasdicity and autocorrelation (HAC) robust estimator which only results in little loss
of efficiency or recursively updating the regression equation for horizon ℎ by including the
residuals from the regression estimated for ℎ − 1.
5- Empirical Results
To illustrate the transmission of interest rate surprises, two sets of data are considered for
computing impulse response functions. The first set of data includes the constructed series for
monthly real GDP, the GDP Deflator and the series for the exchange market pressure index in
addition to the real private credit and a short-term repo rate. Following the common practice of
the literature, the second data set uses the industrial production and consumer price indices
rather than our measure of monthly GDP and the GDP deflator in order to approximate the
broad macroeconomic conditions in Turkey. This second data set also replaces the exchange
market pressure index with the real exchange rate. The real private credit and the repo rate at
7 to 14 days maturities are the common series in both data sets.
The impulse response functions are computed by using two different estimation strategies as
discussed in the previous section. The first estimation strategy estimates VAR models and
attempts to find out the average response of the economy over a 16-month horizon. In the
second strategy, which is the local projections, we use the same structural interest rate shocks
obtained from the estimation of the VAR models. Then we estimate direct forecast equations
for each variables up to 16 periods ahead starting from 2012:5 until 2013:9 and construct the
impulse responses as a sequence of the estimated coefficients of the shock series. The
structural interest rate shocks used in the local projections are presented in Figure 2. This figure
suggests that two important events of monetary contractions, in mid-2006 and in the last
months of 2011, came as policy shocks, which can be observed as jumps in the shock series.
Figure 3 displays the responses of the first set of variables to one-standard deviation of interest
rate shocks. The first column of Figure 3 draws the impulse response functions obtained from
the VAR model and the second column shows the impulse response functions from the local
projections. It is striking to note that there are some similarities across these responses, which
lead us to establish several conclusions about the dynamics of the Turkish economy. First of all,
there is no indication of the “price puzzle” in the low inflation period. An unexpected interest
rate shocks induces a decrease, albeit a sluggish one, in the price level as predicted by the
mainstream models. However, the significant responses from the local projections and the VAR
model fade away after a year. Secondly, there seems to be faster responses of the economic
activity to an interest rate shock than the response of the pricing behavior. After an interest
rate shock, the economic activity initially exhibits a small jump and then declines in the
13
following first few months. However, this decline reverses its direction after 4 months and
remains insignificant thereafter. Thirdly, a negative impact appears to be also observed in the
responses of private credits to the interest rate shocks. This time, however, the real private
credits permanently remain below its initial level rather than revert back as observed in the
economic activity. This finding provides some support for the credit view of the monetary
transmission mechanism, which is emphasized in a number of papers as the main working
channel in Turkey (see Başcı et al., 2007).
The exchange rate puzzle, which are widely reported for small open economies, is not evident
in Figure 3. The results from local projection shows that a surprise increase in short-term
interest rates leads to a moderate appreciation in the Turkish lira against the basket. When we
use the VAR model, however, the responses are not significant across the 16-month horizon. A
negative response of the EMP index, on the other hand, indicates either an accumulation of
international reserves or exchange rate appreciations as expected after an increase in interest
rates.
Another important finding in Figure 3 is that interest rate shocks do not immediately fade away
and remain significant at least four months after the initial impact if we consider the local
projections. This finding is consistent with the monetary policy implementations of the Central
Bank of Turkey, especially after the global financial crisis of late 2008. With its new policy
framework the Central Bank of Turkey allows interest rate to be determined within a wide
interest rate corridor, which gives some flexibility to short-term interest rates to move away
from the policy rate for a considerable period of time. Under strict interest rate control, on the
other hand, these kind of exogenous interest rate shocks would be immediately accommodated
by policy actions in order to prevent it from diverging from the policy rate.
Figure 4 conducts the same analysis by using the data set that includes the consumer price
index, the industrial production index, real private credit, real exchange rate and the repo rate
at 7 to 14-day maturities. As in the previous figure, the results do not produce any dynamics
that can be interpreted as an indication of the “price puzzle”. However, the response of the
consumer price level appear to be much faster than the responses of the GDP deflator in Figure
3. As seen in Figure 3 the peak response of the GDP deflator occurs after 9 months in
comparison to 6 months for the consumer price index in figure 4.
Furthermore, in this figure the negative responses of the Industrial Production Index and the
real private credit seem to occur later than the response of the consumer prices. However, this
findings are not in line with the existing theories which predicts faster output and sluggish price
responses. Figure 4 also shows that the “exchange rate puzzle” seems to return with the
inclusion of the real exchange rate as a measure of the exchange market pressure. A positive
interest rate surprise tends to be followed by a gradual real depreciation of the exchange rate
as implied by the IRFs obtained from both the VAR model and the local projections.
14
It is also interesting to note that similar results are reported in other papers that analyze the
transmission channels of monetary policy with the conventional series covering the low
inflation period in Turkey. For example, in contrast to the predictions of the existing theories
the results presented in Çatik and Martin (2012) indicate that the response of the consumer
price index is much faster than the response of the industrial production index. Moreover, their
results also confirm our conclusion in that the “exchange rate puzzle” remains to be a problem
in the low inflation period when the real exchange rate is used to summarize the forces in the
FX market. The results presented in Figure 3, on the other hand, seem to provide solutions to
these anomalous dynamic responses.
In summary, the impulse response functions computed by using the data set that includes our
newly introduced series for real GDP, the GDP deflator and the EMP index are empirically more
plausible than the functions derived from using the data set that includes the conventional
series. In particular, the results from Figure 3 do not exhibit dynamic patterns that can be
interpreted as puzzling dynamics for the price level and exchange rates. A surprise increase in
the short-term interest rates tends to be followed by a slow but significant decline in the GDP
deflator. The responses of the exchange rate are also found to be in line with the theory when
we use the EMP index for summarizing the exchange market movements. As a result we argue
that approximating the economic activity, price and exchange market developments with the
industrial production index, the consumer price index and real exchange rate produces
misleading conclusions about the monetary transmission mechanism since the impulse
response functions derived from the models that employ these variables may not be able to
capture the full macroeconomic response of the Turkish economy to an interest rate shock.
6- Conclusion
In this paper, we have investigated the monetary transmission mechanism by focusing on the
macroeconomic responses to interest rate surprises. Our starting point was to construct a new
monthly data set rather than just relying on the conventional series such as the industrial
production index, the CPI and the real exchange rate. In particular, we have interpolated the
sub-items in the national income accounts and then constructed monthly GDP and the GDP
deflator series by employing the Proportional Denton First Difference Method. To measure the
exchange market movements we have constructed an EMP index, which has some advantages
over the real or nominal exchange rate when central banks intervene in the FX markets.
After constructing the new data set, we have estimated VAR models by using both these series
and the conventional series. Our identification scheme used in the VAR models have been
based on a semi-structural VAR approach by ordering the interest rate last. Additionally, for
each variable included in our data sets we have run multi-step direct forecast regressions,
which are referred as local projections, in order to check the robustness of the impulse
responses obtained from the VAR models. It is argued that the impulse responses derived from
local projections are consistent, asymptotically normal and perform better in the presence of
misspecification problems.
15
The impulse response functions shows that the price and exchange rate puzzles are not evident
during the relatively low inflation period in Turkey after 2004 when the data set includes the
newly introduced series. The response of the GDP deflator to a contractionary monetary policy
shock exhibits a dynamic pattern which is more in line with the mainstream economic theory. It
responds very slowly at the initial periods and becomes only significant in the third quarter
following the shock. The Turkish lira also slightly appreciates after an interest rate shock. The
evidence is mixed if we use the conventional series. In contrast to the predictions of the theory,
the impulse response functions show much faster responses for the consumer price index
fallowed by a decline in the economic activity. Moreover, the “exchange rate puzzle” remains
to exist even in the low inflation regime.
16
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18
Table 1- Indicator Variables for Estimating Monthly GDP and Deflator Series
Estimated Variable
Indicator Variable
GDP Deflator
- First Principal Component from Producer Price Index,
Domestic Producer Price Index and Consumer Price Index
- Personnel Expenditures from Central Government
Budget
- Deflated by Public Sector Deflator
- Purchase of Goods and Services from Central
Government Budget
- Deflated by Public Sector Deflator
- Central Government Investment Expenditures from
Central Government Budget
- Deflated by Public Sector Deflator
- Whole Sale Prices –Government Sector – before 2005
- Producer Price Index – after 2005
- Industrial Production Index
- Machinery Investment
- Industrial Production Index
- Metal Production other than Machinery
- Tax revenue on Domestically Sold Goods and Services
from Central Government Budget
- Deflated by Consumer Price Index
- Export Quantity Index
- Import Quantity Index
- Monthly seasonal pattern of “Economic Tendency
Survey- Question: Current stock Level Compared to
Normal” - after 2007
- Monthly seasonal pattern of “Economic Tendency
Survey” is extrapolated backward for the period before
2007.
Nominal Personnel
Expenditures - Government
Nominal Purchase of Goods
and Services-Government
Nominal Total Investment –
Government
Public Sector Deflator
Real Machinery Investment
– Private Sector
Real Construction
Investment– Private Sector
Real Consumption – Private
Sector
Real Export
Real Import
Real Stock Changes
19
Correlation
Level
Change
0.99
0.64
0.99
0.94
0.97
0.97
0.84
0.84
0.99
0.22
0.97
0.84
0.87
0.11
0.91
0.59
0.95
0.99
0.34
0.49
0.94
0.10
2.1000
1.9000
1.7000
1.5000
1.0000
0.5000
0.0000
-0.5000
-1.0000
30.0000
25.0000
20.0000
15.0000
10.0000
5.0000
0.0000
2004m1
2004m7
2005m1
2005m7
2006m1
2006m7
2007m1
2007m7
2008m1
2008m7
2009m1
2009m7
2010m1
2010m7
2011m1
2011m7
2012m1
2012m7
2013m1
2013m7
2004m1
2004m7
2005m1
2005m7
2006m1
2006m7
2007m1
2007m7
2008m1
2008m7
2009m1
2009m7
2010m1
2010m7
2011m1
2011m7
2012m1
2012m7
2013m1
2013m7
2.3000
2004m1
2004m7
2005m1
2005m7
2006m1
2006m7
2007m1
2007m7
2008m1
2008m7
2009m1
2009m7
2010m1
2010m7
2011m1
2011m7
2012m1
2012m7
2013m1
2013m7
2004m1
2004m7
2005m1
2005m7
2006m1
2006m7
2007m1
2007m7
2008m1
2008m7
2009m1
2009m7
2010m1
2010m7
2011m1
2011m7
2012m1
2012m7
2013m1
2013m7
2.5000
2004m1
2004m7
2005m1
2005m7
2006m1
2006m7
2007m1
2007m7
2008m1
2008m7
2009m1
2009m7
2010m1
2010m7
2011m1
2011m7
2012m1
2012m7
2013m1
2013m7
2004m1
2004m7
2005m1
2005m7
2006m1
2006m7
2007m1
2007m7
2008m1
2008m7
2009m1
2009m7
2010m1
2010m7
2011m1
2011m7
2012m1
2012m7
2013m1
2013m7
2.7000
2004m1
2004m7
2005m1
2005m7
2006m1
2006m7
2007m1
2007m7
2008m1
2008m7
2009m1
2009m7
2010m1
2010m7
2011m1
2011m7
2012m1
2012m7
2013m1
2013m7
2004m1
2004m7
2005m1
2005m7
2006m1
2006m7
2007m1
2007m7
2008m1
2008m7
2009m1
2009m7
2010m1
2010m7
2011m1
2011m7
2012m1
2012m7
2013m1
2013m7
Figure 1- Variables Used in the VAR models and Local Projections
Monthl y GDP Defl ator
16.5000
Monthl y GDP
16.4000
16.3000
16.2000
16.1000
16.0000
15.9000
15.8000
15.7000
15.6000
15.5000
2.5000
EMP Index
16.0000
Real Pri vate Credi t
2.0000
15.5000
1.5000
15.0000
14.5000
14.0000
13.5000
13.0000
-1.5000
12.5000
-2.0000
12.0000
-2.5000
11.5000
Short-term Interest Rates
5.5000
Consumer Pri ce Index
5.4000
5.3000
5.2000
5.1000
5.0000
4.9000
4.8000
4.7000
4.6000
4.5000
5.0000
Industri al Producti on Index
5.0000
Real Exchange Rate
4.9000
4.9500
4.8000
4.9000
4.7000
4.8500
4.6000
4.8000
4.5000
4.7500
4.4000
4.7000
4.3000
4.6500
4.2000
4.6000
4.1000
4.5500
4.0000
4.5000
20
Figure 2- Changes in Short-term Interest Rates and Surprise Component of Monetary Policy
Monetary Policy Shocks
Monthly Interest Rate Changes
Interest Rate Surprises - Constructed Series
Interest Rate Surprises - Conventional Series
2.5000
1.5000
0.5000
-0.5000
-1.5000
21
2012m5
2012m1
2011m9
2011m5
2011m1
2010m9
2010m5
2010m1
2009m9
2009m5
2009m1
2008m9
2008m5
2008m1
2007m9
2007m5
2007m1
2006m9
2006m5
2006m1
2005m9
2005m5
2005m1
2004m9
2004m5
2004m1
-2.5000
Figure 3- Impulse Response Functions - [Monthly GDP, Monthly GDP Deflator, Real Credit, EMP and Interest Rate]
Constant Parameter VAR Model
Local Projections
Response of l n(GDP Defl ator) to Interest Rate Shock
Response of l n(GDP Defl ator) to Interest Rate Shock
0.0035
0.0020
0.0015
0.0010
0.0005
0.0000
-0.0005
-0.0010
-0.0015
-0.0020
-0.0025
-0.0030
0.0015
-0.0005
-0.0025
-0.0045
-0.0065
-0.0085
-0.0105
2013m6
2013m7
2013m8
2013m9
2013m7
2013m8
2013m9
2013m5
2013m6
Response of l n(GDP) to Interest Rate Shock
2013m4
10 11 12 13 14 15 16
2013m3
9
2013m2
8
2013m1
7
2012m12
6
2012m11
5
2012m9
4
2012m10
3
2012m8
2
2012m7
1
2012m6
0
2012m5
-0.0125
Response of ln(GDP) to Interest Rate Shock
0.0175
0.0065
0.0125
0.0045
0.0075
0.0025
7
8
9
10 11 12 13 14 15 16
Response of l n(Real Credi t) to Interest Rate Shock
2013m5
6
2013m4
5
2013m3
4
2013m2
3
2013m1
2
2012m12
1
2012m11
0
2012m10
-0.0175
-0.0075
2012m9
-0.0125
-0.0055
2012m8
-0.0075
-0.0035
2012m7
-0.0015
2012m5
-0.0025
2012m6
0.0025
0.0005
Response of ln(Real Credit) to Interest Rate Shock
0.0010
0.0100
0.0000
0.0050
-0.0010
-0.0020
0.0000
-0.0030
-0.0050
-0.0040
-0.0100
-0.0050
Response of EMP to Interest Rate Shock
2013m9
2013m8
2013m7
2013m6
10 11 12 13 14 15 16
2013m5
9
2013m4
8
2013m3
7
2013m2
6
2013m1
5
2012m12
4
2012m11
3
2012m10
2
2012m9
1
2012m8
0
2012m7
2012m5
-0.0070
2012m6
-0.0150
-0.0060
Response of EMP to Interest Rate Shock
Response of Interest Rate to Interest Rate Shock
2013m9
10 11 12 13 14 15 16
2013m9
9
2013m8
8
2013m8
7
2013m7
6
2013m7
5
2013m6
4
2013m5
3
2013m4
2
2013m3
1
2013m2
0
2013m1
-0.1000
2012m12
2012m5
-0.0800
2012m11
-0.0600
2012m10
-0.0400
2012m9
0.0000
-0.0200
2012m8
0.0200
2012m7
0.0400
2012m6
0.2500
0.2000
0.1500
0.1000
0.0500
0.0000
-0.0500
-0.1000
-0.1500
-0.2000
-0.2500
0.0600
Response of Interest Rate to Interest Rate Shock
0.6000
0.6000
0.5000
0.4000
0.4000
0.2000
0.3000
0.0000
0.2000
9
10 11 12 13 14 15 16
22
2013m6
8
2013m5
7
2013m4
6
2013m3
5
2013m2
4
2013m1
3
2012m12
2
2012m11
1
2012m10
0
2012m9
-0.6000
-0.2000
2012m8
-0.4000
2012m5
0.0000
-0.1000
2012m7
0.1000
2012m6
-0.2000
Figure 4- Impulse Response Functions -[CPI, IPI, Real Credit, RER and Interest Rate]
Constant Parameter VAR Model
Local Projections
Response of l n(CPI) to Interest Rate Shock
Response of l n(CPI) to Interest Rate Shock
2013m6
2013m7
2013m8
2013m9
2013m7
2013m8
2013m9
2013m5
2013m6
Response of l n(IPI) to Interest Rate Shock
2013m4
10 11 12 13 14 15 16
2013m3
9
2013m2
8
2013m1
7
2012m12
6
2012m11
5
2012m9
4
2012m10
3
2012m8
2
2012m7
1
2012m6
0
2012m5
0.0025
0.0015
0.0005
-0.0005
-0.0015
-0.0025
-0.0035
-0.0045
-0.0055
-0.0065
-0.0075
0.0010
0.0005
0.0000
-0.0005
-0.0010
-0.0015
-0.0020
-0.0025
-0.0030
-0.0035
-0.0040
Response of l n(IPI) to Interest Rate Shock
0.0040
0.0175
0.0125
0.0020
0.0075
0.0025
0.0000
-0.0025
-0.0020
-0.0075
-0.0125
-0.0040
Response of l n(Real Credi t) to Interest Rate Shock
2013m5
10 11 12 13 14 15 16
2013m4
9
2013m3
8
2013m2
7
2013m1
6
2012m12
5
2012m11
4
2012m10
3
2012m9
2
2012m8
1
2012m7
0
2012m6
2012m5
-0.0175
-0.0060
Response of l n(Real Credi t) to Interest Rate Shock
0.0020
0.0075
0.0010
0.0000
0.0025
-0.0010
-0.0020
-0.0025
-0.0030
-0.0075
-0.0040
-0.0125
-0.0050
Response of l n(RER) to Interest Rate Shock
2013m9
2013m8
2013m7
2013m6
10 11 12 13 14 15 16
2013m5
9
2013m4
8
2013m3
7
2013m2
6
2013m1
5
2012m12
4
2012m11
3
2012m10
2
2012m9
1
2012m8
0
2012m7
2012m5
-0.0070
2012m6
-0.0175
-0.0060
Response of l n(RER) to Interest Rate Shock
0.0100
0.0150
0.0100
0.0050
0.0050
0.0000
-0.0050
0.0000
-0.0100
-0.0150
-0.0200
-0.0050
2013m7
2013m8
2013m9
2013m8
2013m9
2013m6
2013m7
Response of Interest Rate to Interest Rate Shock
2013m5
10 11 12 13 14 15 16
2013m4
9
2013m3
8
2013m2
7
2013m1
6
2012m12
5
2012m11
4
2012m10
3
2012m9
2
2012m8
1
2012m7
0
2012m6
2012m5
-0.0250
-0.0100
Response of Interest Rate to Interest Rate Shock
0.6000
0.7000
0.5000
0.5000
0.4000
0.3000
0.3000
0.2000
0.1000
0.1000
-0.1000
0.0000
23
2013m6
10 11 12 13 14 15 16
2013m5
9
2013m4
8
2013m3
7
2013m2
6
2013m1
5
2012m12
4
2012m11
3
2012m10
2
2012m9
1
2012m8
0
2012m7
-0.2000
2012m6
2012m5
-0.3000
-0.1000
Central Bank of the Republic of Turkey
Recent Working Papers
The complete list of Working Paper series can be found at Bank’s website
(http://www.tcmb.gov.tr).
Firma Maliyet Yapısı ve Maliyet Kaynaklı Enflasyon Baskıları
(Hatice Burcu Gürcihan Yüncüler, Fethi Öğünç Çalışma Tebliği No. 15/03, Ocak 2015)
Taxing Fossil Fuels under Speculative Storage
(Semih Tümen, Deren Ünalmış, İbrahim Ünalmış, D. Filiz Ünsal Working Paper No. 15/02, January 2015)
Türkiye’de Şirketlerde Borç Dolarizasyonu ve Büyüme Performansı
(Bengü Alp, Cihan Yalçın Çalışma Tebliği No. 15/01, Ocak 2015)
Joint Dynamics of House Prices and Foreclosures
(Yavuz Arslan,Bülent Güler, Temel Taşkın Working Paper No. 14/40, December 2014)
On the Uncertainty-Investment Relationship: An Overview with an Application to the Power Plant Investments in Turkish
Electricity Sector
(Erdal Yılmaz , Working Paper No. 14/39, November 2014)
Rezerv Opsiyon Mekanizması Otomatik Dengeleyici İşlevi Görüyor Mu?
(Oğuz Aslaner , Uğur Çıplak , Hakan Kara , Doruk Küçüksaraç , Working Paper No. 14/38, October 2014)
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(Oğuz Atuk, Cem Aysoy, Mustafa Utku Özmen, Çağrı Sarıkaya Çalışma Tebliği No. 14/37, Eylül 2014)
On International Consumption Risk Sharing, Financial Integration and Financial Development
(Yasin Mimir Working Paper No. 14/36, September 2014)
Unemployment Flows, Participation and the Natural Rate for Turkey
(Gönül Şengül, Murat Taşçı Working Paper No. 14/35, September 2014)
Firm-Size Wage Gaps along the Formal-Informal Divide: Theory and Evidence
(Binnur Balkan, Semih Tümen Working Paper No. 14/34, September 2014)
Sectoral Asymmetries in a Small Open Economy
(S. Tolga Tiryaki Working Paper No. 14/33, September 2014)
Consumer Tendency Survey of Turkey: A Disaggregated Analysis
(Ece Oral, Türknur Brand Working Paper No. 14/32, September 2014)
Asymmetric Exchange Rate and Oil Price Pass-Through in Turkish Fuel Oil Market
(Fatih Akçelik, Mustafa Utku Özmen Working Paper No. 14/31, September 2014)
Turkish Middle Income Trap and Less Skilled Human Capital
(Gökhan Yılmaz Working Paper No. 14/30, September 2014)
The Structure of the Turkish Banking Sector Before and After the Global Crisis
(Aytül Ganioğlu , Vuslat Us Working Paper No. 14/29, August 2014)
Determinants of Bond Flows to Emerging Markets: How Do They Change Over Time?
(Yasemin Erduman, Neslihan Kaya Working Paper No. 14/28, August 2014)