Measuring the Output Gap for Turkish EconomyA Preliminary Work+
Hande AVŞAR∗, Yasemin GİRİCİ∗, Zeynep ÖZATAY ∗
Abstract This paper is a preliminary work on the estimation of potential output of Turkish economy for the period 1987Q1-2007Q3. Two statistical methods of estimating output gap and application of these to the Turkey are presented in the paper. Vector Error Correction Model and univariate Kalman Filter techniques are employed, then each method’s results are presented. The estimation findings imply similar pattern of cyclical movements of Turkish economy with the existing literature. Although all methods give a consistent pattern of business cycle with each other, there has been a divergence both in sign and magnitude of two methods. In addition it is verified that Turkish economy has entered a new era of economic stability after many years of fluctuating output. Keywords: Output Gap, VECM, Kalman Filter. JEL Classification: C51, C22, E32. *
Undersecretariat of Turkish Treasury. E-mail to
[email protected],
[email protected],
[email protected] +
We would like to thank to D. Bahar Özgün Yılmaz for her valuable comments. The views expressed in this paper are those of the authors and do not necessarily correspond to the views of the Turkish Treasury. Any errors are our own.
Contents 1. Introduction........................................................................................................2 2. Data .....................................................................................................................6 3. Methodology .......................................................................................................6 3.1. Vector Error Correction (VEC) Model ..................................................... 6 3.2. Univariate Kalman Filter......................................................................... 10 4. Empirical Results .............................................................................................13 4.1. Vector Error Correction Model (VECM)................................................ 13 4.2. Univariate Kalman Filter......................................................................... 18 4.3. Comparing Results .................................................................................. 19 5. Conclusion ........................................................................................................21 References.............................................................................................................23 Figures 1. Various Indicators for Real Consumption ................................................... 9 2. Estimated Output Gap with Model 1 ......................................................... 17 3. Real GDP and Estimated Potential Output with Model 1 ......................... 17 4. Estimated Output Gap with Model 2 ......................................................... 18 5. Real GDP and Estimated Potential Output with Model 2 ......................... 18 6. Estimated Output Gap with Kalman Filter ................................................ 19 7. Real GDP and Estimated Potential Output with Kalman Filter................. 19 8. Output Gap Estimates with Univariate Kalman Filter and VECM ........... 20 Tables 1. Unit Root Test Results..................................................................................8 2. Lag Order Selection ....................................................................................14 3. Johansen Cointegration Test Results ..........................................................15 4. Cointegration Vector Estimation Results ...................................................16 5. Peaks and Troughs in Economic Activity...................................................21 6. Expansion and Recession Periods of Economic Activity (in quarters) ......21
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1. Introduction The output gap is defined as the deviation of actual output from its potential level. In other words, the output gap denotes the spare/excess capacity in the economy. The gap is negative when actual output falls below the economy's potential. Reciprocally, positive output gap means that the economy operates above its potential level. As the economy can operate above/below its long-run trend in the short run, output gap is an important indicator of the cyclical position of the economy. Research on output gap probably got attention first by Okun (1962) and since then it’s a subject under spotlight both for researchers and policy makers. Estimating potential output and accordingly output gap became a popular phenomenon in recent decades in order to test the reliability of growth and inflation forecasts, estimate structural budget balance, and assess the stance of monetary policy. Rising importance of achieving medium term price stability has generated the need to access all available information concerning the state of the economy. To this end, output gap estimations are widely used in central bank’s monetary policy response function such as in the Taylor rule or in the inflation targeting (IT) framework. When the economy operates above its capacity in the short run, this tends to put upward pressure on prices. Therefore, if the output gap is positive, central bank is generally forced to raise short term interest rates for preventing the economy from overheating. Taylor (1993) introduced a rule in order to determine the future course of US benchmark interest rate using inflation deviation from its target level and output deviation from its potential. Svensson (1999) mentioned the importance of output gap in inflation targeting framework. Furthermore, Gerlach and Svensson (2003) suggested the use of output gap as a predictor of inflation rather than money growth for Euro Area.
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Both the level of potential output and the output gap are unobserved, so only the estimates are available. Therefore, predicting the state of the economy as precisely as possible poses a major challenge especially for policy makers. Because of their unobserved nature, in literature several methods are used to estimate the potential output, hence the output gap. Existing literature indicates that, output gap varies from method to method. Cotis et al. (2003) compared different estimation methods and found that in general, methods provide estimates with a similar pattern of potential output, but there are large divergences on the assessment of the magnitude of the output gap. Moreover, after analyzing five European countries, namely Finland, France, Greece, Italy, and the United Kingdom, Billmeier (2004) concluded that there is no single best gap measure across these countries. Besides, Scacciavillani and Swagel (1999) also found estimates of potential output in Israel vary by the methodology chosen. In the literature on this subject, it is clear that researchers usually preferred to estimate the gap by different methods instead of relying on a single measure since as mentioned above there is no ideal method for measuring the gap. Using HP filter, BN decomposition, unobserved component models, SVAR and production function methods in order to estimate the state of the Sweden economy, Cerra and Saxena (2000) remarked that each one has both advantages and disadvantages. Gradzewicz and Kolasa (2005) also mentioned about the strong assumptions needed during the estimation process so drawing any conclusions should be taken with caution. Furthermore, in Bjørnland et al. (2005), it’s said that professional judgment was needed to analyze the results of each technique and draw any conclusion from them. Approaches to estimating output gap may be separated into two main genres: statistical and structural ones.
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Statistical approaches treat potential output as a trend and actual level of output as fluctuating around this trend. Therefore, potential output is estimated using the past behaviour of actual output level. There are various decomposition methods for detaching trend and cycle which have different assumptions about the nature of the output. Trend component reflects the evolution of the variables that affect activity in the long run, whereas cyclical component reflects short-term fluctuations around the trend. Linear trends, univariate filters (Hodrick and Prescott (HP), Band-Pass Filter and Beveridge and Nelson (BN)), and unobserved components models (State-Space with Kalman Filter) are most commonly used statistical methods. Estimation methods based on economic theory generally use a structural economic model for the estimation of potential output and take into account the interaction of different economic variables. These methods are generally based on economic theory along with the statistical tools. But the results may vary depending on the model selected and strict assumptions made. Structural Vector Auto Regression (SVAR) and production function approaches are the main ones based on economic theory. Economic literature consists of many studies using different types of these methods for estimating output gap of various countries. For example, De Masi (1997) applied several techniques for G-7 and developing countries. He estimated the potential output by production function for industrial countries, while preferred statistical approaches for developing ones. Compared to works done for other countries, there are limited studies in estimating potential output of Turkish economy. Öğünç and Ece (2004) used basic univariate and bivariate unobserved components models from 1987:q1 to 2002:q4. They also constructed confidence bands for potential output and output gap. These bands show that 1993 and 1997-1998 are the expansion periods but 1989, 1994 and 2001-2002 are the recession periods of the economy. Moreover,
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they claimed that the relationship between inflation and output gap is limited since inflation is closely tied to exchange rate and past inflation at the period of study. Sarıkaya et al. (2005) employed the extended Kalman Filter in a multivariate framework in that the Phillips curve and output dynamics are included to the state-space system for Turkey. Gap is defined as a function of real interest rate, real effective exchange rate, demand index and it’s own past. The most striking point of their study is that all parameters are time-varying and timeseries specification of each is assumed for estimation. Kaya & Yavan (2007) made use of both statistical and economical approaches to measure the gap for Turkish economy and compared all the results. They looked at the correlation between capacity utilization, which is representing the cyclical movements of the economy, and each gap measures of different methods. Moreover, the relationship between inflation and different gap measures are also explored in the study. At the end, they claimed that the production function technique with Cobb-Douglas has many advantages over other methods although admitting the assumptions are too strict in that approach. As a preliminary step, this study mainly aims to measure the output gap/potential output of Turkish economy by utilizing two methods. As mentioned before, estimated output gaps are sensitive to the methodologies chosen and the assumptions made in the work. Among the approaches available in literature, two statistical ones, namely Vector Error Correction Model (VECM) and unobserved components model with univariate Kalman Filter technique are used in this paper. The estimation findings imply similar pattern of cyclical movements of Turkish economy with the existing literature. Although all methods give a consistent pattern of business cycle with each other, there has been a divergence both in sign and magnitude of different methods. As the same conclusion of each, it is verified that real economic growth has shifted to a more stable pattern since 2001 economic crisis.
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The rest of the paper is organized as follows. The following section presents the data used in two methods. Estimation methodology and the models constructed are shown in section 3. In section 4, empirical findings are reviewed and a comparison of the estimated gap series is displayed. Finally, section 5 concludes.
2. Data Quarterly data from 1987:q1 to 2007:q3 are used for real GDP and consumption in this study. In addition, different components of consumption (non-durable, food and beverages) are utilized in VECM. The data include 83 observations. Real GDP and consumption data are taken from TURKSTAT. The base year of the national accounts is 1987=100. Before applying the empirical models, the series are transformed into log form and adjusted for seasonality. Seasonality is done by using TRAMO/SEATS (Time Series Regression with ARIMA Noise, Missing Observations, and Outliers) method.
3. Methodology
3.1. Vector Error Correction (VEC) Model A vector error correction (VEC) model is a restricted VAR designed for use with non-stationary series that are known to be cointegrated. The VEC has cointegration relations built into the specification so that it restricts the long-run behaviour of the endogenous variables to converge to their cointegrating relationships while allowing for short-run adjustment dynamics. The cointegration term is known as the correction term since the deviation from long-run
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equilibrium is corrected gradually through a series of partial short-run adjustments. A two variable system of cointegrating equation having one cointegrating equation and no lagged difference terms is shown as:
y t = α ct , and the VEC model is:
∆ y t = β 1 ( ct −1 − α y t −1 ) + ε 1t ∆ ct = β 2 ( ct −1 − α y t −1 ) + ε 2 t where y t is real GDP and ct is an indicator of consumption. In this simple model, the only right-hand side variable is the error correction term. In long run equilibrium, this term is zero. However, if y t and ct deviate from the long run equilibrium, the error correction term will be non zero and each variable adjusts to partially restore the equilibrium relation. The coefficients β1 and β 2 measure the speed of adjustment of relevant endogenous variable towards the equilibrium. As the VEC specification only applies to cointegrated series, first the number of (if any) cointegrating relations must be determined by Johansen cointegration test. Firstly, unit root test is applied to both real GDP and real food & beverages consumption in order to identify the cointegration relationship. According to the Augmented Dickey-Fuller (ADF) test, these variables have unit roots as shown in Table 1.
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Table 1. Unit Root Test Results Null Hypothesis: LGDP has a unit root Augmented Dickey-Fuller test statistic Test critical values:
1% level 5% level 10% level
t-Statistic -2.35 -4.09 -3.47 -3.16
1% level 5% level 10% level
t-Statistic -1.79 -2.60 -1.95 -1.61
1% level 5% level 10% level
t-Statistic -2.21 -4.08 -3.47 -3.16
1% level 5% level 10% level
t-Statistic -3.80 -2.59 -1.95 -1.61
Null Hypothesis: D(LGDP) has a unit root Augmented Dickey-Fuller test statistic Test critical values:
Null Hypothesis: LFOOD has a unit root Augmented Dickey-Fuller test statistic Test critical values:
Null Hypothesis: D(LFOOD) has a unit root Augmented Dickey-Fuller test statistic Test critical values:
Before applying the cointegration test, the number of lags included in the unrestricted VAR estimation should be determined by any of Lag Structure tests (Lag exclusion tests, AR roots and lag length criteria). In this work, by using the cointegration relationship between food & beverages consumption and GDP, VECM method is applied to find the permanent component of GDP.
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Stock & Watson (1988) describes the use of multiple aggregate variables to assess the trend component of the output for USA. They test the cointegration relationship between consumption and national income showing that they share a common stochastic trend. In this paper, their approach is applied for Turkish economy which is not done before. Figure 1. Various Indicators for Real Consumption Real GDP and Durable Consumption
10.6 10.5
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Private Consumption
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GDP
Non-Durable Consumption
Food&Beverages
Figure 1 shows the relationship between real GDP and real consumption, durable consumption, non-durable consumption, food & beverages consumption. As seen in Figure 1 above, consumption indicators and GDP are
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Durable Consumption
Real GDP and Food & Beverages Consumption
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Real GDP and Non-Durable Consumption
GDP
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Real GDP and Consumption
moving together through time and seem to be having common trend. Hence, consumption might be used to estimate the potential GDP by VECM method. In this context, various components of real consumption are reviewed to find the most relevant component of consumption for estimation. As can be seen real food & beverages consumption exhibits a smoother pattern whereas other indicators fluctuates more around real GDP. Also, volatility of food & beverages consumption is low since it is relatively less affected by cyclical movements of economic activity. Therefore, real food & beverages consumption is chosen to estimate the potential GDP by VECM method1.
3.2. Univariate Kalman Filter State space models are applied for modelling unobserved variables like expectations, measurement errors, missing observations, permanent income, unobserved components and the non-accelerating rate of unemployment. Since the potential output vis-à-vis output gap are unobserved by nature, state space models are suitable to measure them. State space models have two equations: a measurement equation and a transition equation. Measurement equation describes the relation between observed variables ( yt ) and unobserved state variables ( at ). Transition equation has the form of a first-order difference equation in the state vector. Measurement equation is:
y t = za t + et et ~ N ( 0 , H ) Transition equation is: 1
It should be noted that the choice of different consumption indicators does not change the output gap estimation results substantially.
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a t = Ta t −1 + u t u t ~ N (0, Q ) where H and Q are the variance matrixes for observed and unobserved variables. The disturbance vectors et and ut are assumed to be serially independent, with contemporaneous variance structure. The main advantage of using state space representation is that unobserved variables are estimated along with the observable model. Also, the model can be analyzed using Kalman filter. The model to estimate output gap used in this paper is the following: Actual Output Identity:
y t = y t* + gapt
(1)
Potential Output Equation:
y t* = y t*−1 + η t
(2)
Output Gap Equation:
gapt = φ1 gapt −1 + ε t
(3)
where y t is the seasonally adjusted real GDP in log form, y t* is the potential output, gapt is the output gap. η t and ε t represents shocks to the system assumed to be identically and independently distributed (iid) with zero mean and constant variance. Equation (1) is identity showing output consists of potential output and output gap. Equation (2) represents potential output as a random walk process. Equation (3) defines output gap as a first order autoregressive process.
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In state space representation shown below, the vector of observed variables (output) are denoted as xt , while the vector of unobserved state variables (potential output and output gap) are denoted by at . The measurement equation, where observed variables denoted as a function of unobserved variables, and transition equation, where state variables are as a function of its past, can be written as follows:
xt = Zat + et at = Ta t −1 + u t
(4) (5)
where et and ut are vectors of normally distributed iid shocks which are assumed to be uncorrelated. The equations (1)-(3) can be rewritten as:
y t* [ yt ] = [1 1] gapt y t* 1 0 y t*−1 η t + = gapt 0 φ gapt −1 ε t where y t* η 1 0 and u t = t . xt = [ y t ], Z = [1 1] , a t = , T = 0 φ ε t gap t
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(6) (7)
Once the system of equations is represented in a state-space form, the estimation procedure is done by using Kalman Filter algorithm with Maximum Likelihood. Furthermore, estimate of potential output is improved by using fixed interval-smoothing algorithm which is utilizing information both in past and future. So the estimation result is made smoother than the ordinary Kalman Filter estimates.2
4. Empirical Results
4.1. Vector Error Correction Model (VECM) The trend in GDP is measured by using the cointegration relationship between GDP (lgdp_sa) and food & beverages consumption (lfood_sa). It is found that there is a common trend between GDP and food consumption. Then, the cyclical component of GDP is estimated using the estimated common stochastic trend. Before determining the cointegration vector, the number of lags is determined with VAR Lag Order Selection Criteria. The results are presented in Table 1.
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In order to run Kalman Filter algorithm initial value of state vector a t , state vector precision
matrix and Q variance matrix should be defined as prior. It’s seen that chosen prior for variance matrix is important in estimation results importantly.
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Table 2. Lag Order Selection VAR Lag Order Selection Criteria Endogenous variables: LGDP_SA LFOOD_SA Exogenous variables: C Sample: 1987:1 2007:3, Included observations: 73 Lag 0 1 2 3 4 5 6 7 8 9 10
LogL 183.43 372.21 373.40 374.38 383.17 389.81 390.22 393.69 395.74 397.92 400.95
LR NA 362.03 2.22 1.77 15.42 11.27* 0.68 5.52 3.14 3.22 4.33
FPE 0.00 0.00 0.00 0.00 0.00 0.00* 0.00 0.00 0.00 0.00 0.00
AIC -4.97 -10.03 -9.96 -9.87 -10.00 -10.08* -9.98 -9.96 -9.91 -9.86 -9.83
SC -4.91 -9.63 -9.64* -9.43 -9.44 -9.39 -9.16 -9.02 -8.84 -8.67 -8.52
HQ -4.95 -9.79 -9.83* -9.70 -9.78 -9.80 -9.65 -9.59 -9.49 -9.39 -9.31
* indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error, AIC: Akaike information criterion, SC: Schwarz information criterion and HQ: Hannan-Quinn information criterion.
As it can be seen in Table 2, the lag order differs according to selection criteria. While LR, FPE and AIC refers to 5 lags, SC and HQ shows 2 lag should be included. Therefore, both 2 and 5 lag orders are used in cointegration test. Johansen Cointegration Test is used to find out whether there is a cointegration between real income and food consumption. Table 3 represents the results of the cointegration test for 2 and 5 lag orders.
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Table 3. Johansen Cointegration Test Results Sample: 1987:1 2007:3 Series: LGDP_SA LFOOD_SA Lags interval: 1 to 2 Data Trend: None None Linear Linear Quadratic Rank or No Intercept Intercept Intercept Intercept Intercept No. of CEs No Trend No Trend No Trend Trend Trend 0 -9.56 -9.56 -9.56 -9.56* -9.45 1 -9.45 -9.40 -9.44 -9.43 -9.38 2 -9.25 -9.22 -9.22 -9.21 -9.21 Lags interval: 1 to 5 Data Trend: None None Linear Linear Quadratic Rank or No Intercept Intercept Intercept Intercept Intercept No. of CEs No Trend No Trend No Trend Trend Trend 0 -9.30 -9.30 -9.33* -9.33 -9.22 1 -9.22 -9.16 -9.22 -9.18 -9.12 2 -9.01 -8.99 -8.99 -8.94 -8.94
The results of Johansen both for 2 and 5 lag orders support the hypothesis that there is one cointegrating relationship between national income and food consumption. In other words, there exists a linear combination of these two difference-stationary series so that the residuals obtained from this linear combination are stationary. This result implies that in the long run GDP and food consumption have the common stochastic trends. According to the Johansen Cointegration Test, there is one cointegration vector including intercept and no trend in VAR with lag 5. On the other hand, the test results in a cointegration vector with an intercept and trend in VAR with lag 2. The cointegrating coefficients derived from VEC estimations are represented in Table 4.
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Table 4. Cointegration Vector Estimation Results Model 1 Method: VECM Lag Order=2 variable constant trend LFOOD_SA (-1)
coefficient -2.47 0.00 -0.86
std. error
t-statistics
0.00 -0.29
-2.96 -2.95
std. error
t-statistics
-0.09
-18.65
Model 2 Method: VECM Lag Order=5 variable constant LFOOD_SA (-1)
coefficient 4.86 -0.88
Then, coefficients derived from cointegrating equations are used to estimate the output gap. Two models and the results are summarized below: Model 1 shows a 2 lag VAR including a cointegration with intercept and no linear trend. According to model results, the estimated output gap is shown in Figure 2. Figure 3 also shows the potential output estimates.
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Figure 2. Estimated Output Gap with Model 1 8 6 4 2
%
0 -2 -4 -6 -8
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Figure 3. Real GDP and Estimated Potential Output with Model 1 10.7 10.6 10.5 10.4
log
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Model 2 shows a 5 lag VECM including a cointegration with intercept and linear trend. According to model results, the estimated output gap is shown in Figure 4.
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Figure 4. Estimated Output Gap with Model 2 12 8
%
4 0 -4 -8
2007Q1
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Figure 5. Real GDP and Estimated Potential Output with Model 2 10.7 10.6 10.5 10.4
log
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4.2. Univariate Kalman Filter By using initial guess of the state vector a 0 and its covariance matrix u 0 applying Kalman Filter and maximum likelihood estimation method, the potential output and gap is shown in Figure 6.
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Figure 6. Estimated Output Gap with Kalman Filter 6.0 4.0 2.0 %
0.0 -2.0 -4.0 -6.0 2007Q1
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Figure 7. Real GDP and Estimated Potential Output with Kalman Filter 10.7 10.6 10.5 10.4 log
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4.3. Comparing Results Turkish economy has exhibited a boom-bust pattern in last decades. The output gap estimates show high volatility in economic activity especially before 2001. Whereas economic instability was dominant in 1989-2001 period, economic activity has relatively stabilized after this period. VECM and univariate Kalman filter methods confirm that Turkish economy has experienced three significant recession periods. Both methods
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demonstrate that Turkish economy experienced recession in 1994, 1998 and 2001. Figure 8 represents the estimated output gaps with these different models together.
Figure 8. Output Gap Estimates with Univariate Kalman Filter and VECM 12 8
%
4 0 -4 -8
1987Q1 1988Q1 1989Q1 1990Q1 1991Q1 1992Q1 1993Q1 1994Q1 1995Q1 1996Q1 1997Q1 1998Q1 1999Q1 2000Q1 2001Q1 2002Q1 2003Q1 2004Q1 2005Q1 2006Q1 2007Q1
-12
kalman filter
vecm-1
vecm-2
The economy is assumed to grow by its potential in the long run, whereas the short run fluctuations in economic activity from its potential level gives an indicator of the business cycle. In line with this literature, the estimated output gap is used to analyze the expansion and recession periods of Turkish economy. Where expansion is defined as the period from trough to peak and recession period is the period from peak to trough. Since estimated output gaps are highly volatile, expansion and recessions are chosen by taking into account of existing literature on Turkish economic history. Although the economic activity exhibits a volatile pattern, recession periods are relatively short by nature. In other words, Turkish economy has been able to recover quickly in last decades. On the average, recessions has lasted 3 to 4 quarters, whereas expansion periods range from 14 to 16 quarters. Results verify that real economic growth has shifted to a more stable pattern after 2001. First, the duration of recessions has shortened, the last
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recession which took place in 2001, lasted only 2 quarters. Secondly, the last expansion period, which has started in 2001, is still continuing. Although both methods indicate a similar timing of cyclical movements, amplitude of peaks and troughs vary across the models. To illustrate, VEC Model 1 estimates a higher output gap in 2001 compared to 1994, on the contrary Kalman Filter and VEC Model 2 give a higher gap in 1994.
Table 5. Peaks and Troughs in Economic Activity Peaks Kalman Filter VECM 1 VECM 2 Troughs Kalman Filter VECM 1 VECM 2
1993 Q2 1998 Q1 1993 Q4 1998 Q1 1994 Q1 1998 Q2
2000 Q4 2000 Q4 2000 Q4
1994 Q2 1999 Q1 1994 Q2 1999 Q1 1995 Q4 1999 Q1
2001 Q2 2001 Q2 2001 Q2
Table 6. Expansion and Recession Periods of Economic Activity (in quarters) Expansion Duration Kalman Filter VECM 1 VECM 2 Recession Duration Kalman Filter VECM 1 VECM 2
1994-98 1999-2000 15 7 15 7 1995-98 1999-2000 10 7
2001-... 25 25 2001-... 25
1993-94 1998-99 2000-01 4 4 2 2 4 2 1994-95 1998-99 2000-01 7 3 2
5. Conclusion The precise measure of the gap is crucial in conducting the right monetary policy. Since Central Bank of Turkey (CBRT) moved to an explicit
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inflation targeting regime in 2006, the importance of accurate measurement of the state of the economy has gained more attention for the last three years. In this context, this paper estimates potential output vis-à-vis output gap by statistical approaches of VECM and Kalman Filter for Turkish economy. Estimation results of both methods demonstrate that Turkish economy experienced recession in 1994, 1998 and 2001. Findings verify that real economic growth has shifted to a more stable pattern after 2001. Although it is found that Turkish economy exhibits a similar pattern of business cycle, there has been a divergence both in sign and magnitude of two different methods after 2001 recession period. Firstly, Kalman Filter estimates an output gap fluctuating around zero for 2002-2007, whereas VECM 1 gives a continuous positive output gap for the last 16 quarters which reaches to 5 percent. On the other hand, estimated output gap in VECM 2 takes values fluctuating from positive 9 percent to negative 2 percent. Furthermore, using VECM 1 magnitude of 2001 recession is found to be deeper than 1994 crisis, while results of Kalman Filter and VECM 2 indicates the reverse. This divergence across different methods poses a challenging monetary environment for policy makers especially in IT framework. The statistical approaches are chosen in this paper over structural ones because their assumptions are less strict and the data availability is higher. However, the findings suggest that output gap estimates should be treated with caution. Assessments of the output gap must also be based on professional judgment and supplementary indicators. Additional economic information may provide some useful information for the estimation of the output gap.
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