




































American Research Journal of Economics, Finance and Management 

Volume 11 Issue 1, January-March 2023 

ISSN: 2836-9416 

Impact Factor: 5.57 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

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14 | P a g e  

MONETARY TRANSMISSION MECHANISMS: INSIGHTS FROM 
THE TURKISH ECONOMY 

 
 

Professor Aylin Sema Erdogdu, 
Istanbul Arel University, Turkey 

 
Abstract: The Monetary Transmission Mechanism (MTM) plays a pivotal role in shaping real 
economic activities, including production, consumption, and employment, by channeling the impact 
of monetary policy decisions. This dynamic process encompasses the total demand resulting from 
these policy decisions, as well as their effects on inflation expectations and rates. In the contemporary 
economic landscape, one of the most prominent repercussions of fluctuations in monetary policy is 
witnessed in the financial choices made by businesses. Such policy changes can significantly influence 
enterprises' sales, production expenditures, and balance sheets, spanning both durable and 
nondurable goods production and impacting households' overall expenditure. 
The MTM can be broadly categorized into two primary components. Firstly, it encompasses the 
analysis of market interest rate fluctuations, delving into how monetary policy decisions affect the 
asset landscape, including foreign exchange rates and financial market conditions. Secondly, it 
scrutinizes the production-related aspects affected by changes in financial market conditions and 
inflation rates. The MTM operates through a multifaceted network of channels, influencing 
households' purchasing decisions and altering firms' balance sheets. 
This article underscores the diverse channels that constitute the MTM, including the exchange rate 
channel, interest rate channel, bank credit channel, and balance sheet channel. By exploring these 
channels, it seeks to unravel the intricate pathways through which monetary changes reverberate 
across total demand and production levels. 
Keywords: Monetary Transmission Mechanism, monetary policy, interest rate channel, exchange 
rate channel, bank credit channel, balance sheet channel. 
  
1. Introduction    
Monetary Transmission Mechanism influences real economic activities such as production, 
consumption and employment through its own dynamics during the implementation of monetary 
policy decisions. More precisely, it can be defined as the total demand of the monetary policy decisions, 
and the process of inflation expectations and inflation rates. Nowadays, the most important effect of 
changes in monetary policy occurring due to macroeconomic fluctuations is observed in the financial 
decisions of companies. Changes in the monetary policy are transmitted both to the sales of enterprises 
which produce durable and non durable goods, through the changes in an enterprise’s total expenditure 
of households, and to the balance sheets of these legal entities. This approach, known as Monetary 
Transmission Mechanism, determines how and to the extent to which monetary changes influence total 
demand and production. Based on these descriptions that are explained above, it can be deduced in 
which way and to what extent monetary changes influence total demand and total production.   

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Monetary transmission mechanism can generally be analyzed under two main headings. The first 
analysis determines the examination of changes in market interest rates. Monetary policy determines 
the transfer of assets such as foreign exchange rates and financial market conditions. The second 
indicates the production level of the changes in financial market conditions and inflation rates. 
Monetary transmission mechanism functions through a number of channels and the tendency of 
households to purchase, and it also influences the balance sheets of firms. Channels affect the 
purchasing power decisions of households and the changes in companies’ balance sheets through 
monetary transmission mechanism. Phases affecting manufacturing industry are the exchange rate 
channel, interest rate channel, the bank credit channel and the balance sheet channel.  
2. Efficiency Analysis of Monetary Transmission Mechanism 
2.1. Literature Review 
When the literature is reviewed on the monetary transmission mechanism, it is observed that Bacchetti 
and Ballabriga (2000) have tested the data for the US and 13 European countries and have reached the 
conclusion that banking loans are affected by the monetary policies. Ferreira (2007), examined the 
bank performance of the credit channels for the European Monetary Union member countries, 
especially for Portugal. Kashyap and Stein (2000) revealed in their studies that small banks have less 
liquidity and play a greater role on credit volume than their larger counterparts. Kishan and Opiela 
(2000) analysed the effects on credit supply through bank assets and bank capital. Butz, Fuss and 
Vermeulen (2001) used all available industrial databases within the Belgian economy to study the 
effects of monetary policy on firm behaviors. Disyatat and Vongsinsirikul (2003) have tested the data 
for the Thailand -2001Q4 - 1993Q1 period. Chirink and Kalckreuth (2003) reviewed the interest rate 
for fixed capital investment firms in Germany to determine the importance of the interest rate channel 
and the credit channel. Yue and Zhou (2007) have tested the data for China 1996.1 - 2005.8 period.  
2.2. Research Model  
This section investigates which channels are active in the Monetary Transmission Mechanism in 
Turkey. In order to examine the effectiveness of the monetary transmission mechanism, as it is a widely 
used method in the literature and provides reliable results, VAR analysis method is utilized. This study 
aims to determine whether the monetary policies used in Turkey after 1990s have affected economic 
development. If they have done so, it will discuss how long these effects last. Monthly time series were 
used, covering the period January 1990 to July 2011.  
The variables used in the model are selected for representing the operation of the monetary 
transmission mechanism. Our dependent variable is money supply (M1). M1 is represented as the cash 
in circulation and defined as the sum of deposits in demand deposits in commercial banks and central 
banks. As an indicators of monetary policy we used, inter-bank interest rate on the market (overnight 
(O / N)), the Istanbul Stock Exchange National 100 Index, the sum of domestic loans in the banking 
sector (in TL), USD buying exchange rate, Consumer Price Index as the inflation rate (CPI), and for the 
real sector; the Industrial Production Index (IPI) used. Detailed description of the variables and 
parameters used in the model and their symbols are shown in Table 1. 
 
 
 

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 Table 1. Variables  
M1     Money Supply TL  
INTEREST    Interbank Overnight Simple Weighted Average Interest Rate Monthly (%)  
BIST      National 100 Index (1986 = 100)  
CREDIT   Total Domestic Banking Sector TL Loans  
EXCHANGE RATE   Central Bank Buying Exchange Rate  
CPI      Consumer Price Index Monthly Change (%) IPI      Industrial Production 
Index  
In this study, / E-Views econometrics software package was used to determine the time-series 
properties of the data related to the variables. To bring all variables to the same level, logarithm1was 
used and the first difference of the logarithmic time series were calculated. Therefore, these variables 
are set to the same level. All variables were seasonally adjusted by using the moving average method.   
The seasonally adjusted time series received "SA" letters at the end of the each variable representation, 
the unstable variables and the variables that were adjusted to be stationary by taking their first 
differences labeled with "D" letter at the beginning of the each variable representation.   
2.3. Working M1 Money Supply Model   
Factors affecting economic variables sometimes create lasting variability in the trends of variables. 
These changes may be caused by the impact of technological developments and events, such as political 
changes. The following figure shows the structural break of our model.  
 Figure 1. Structural Break Graphics 

 
Our model are trying to create a model by using the entire period data that is estimated from monetary 
policy applications from the internal crisis (1994, 2000 and 2001 crises) and external shocks (Asian 
crisis, the Russian crisis, the Brazilian crisis). The effects can be expressed as the structural breaks 
observed. Structural break was corrected using dummy variables. Models and graphics found using 
dummy variables are listed below. Dummy Variable (Dk) i, 1990 December 2001 January 0, 2002 
January - May 2004 1, 2004 and June 0, 2004 July 2004 November 1, 2004 from December 2005 to 
January 0, 2005 February-July 2011, by putting the value of 1. 
 
 

                                                      
1 Log (  ) - log ( ) = log [   /   ]» [(   -   ) /  ]   

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 Table2. Dummy Variables Used Model  
Dependent Variable: LOGM1SA  
Sample (adjusted): 1990M01 2011M07  
Included observations: 259 after adjustments  
  
  

 
    Variable  Coefficient  Std. Error  t-Statistic  Prob.    
  C  6.372066  0.377371  16.88543  0.0000  
  LOGINTERESTSA  -0.118471  0.014437  -8.205939  0.0000  
  LOGBISTSA  0.161689  0.019874  8.135788  0.0000  
  LOGCREDITSA  0.493224  0.019567  25.20640  0.0000  
  LOGEXCHANGESA  0.314467  0.021346  14.73171  0.0000  
  LOGCPISA  -0.052087  0.005200  -10.01651  0.0000  
  LOGIPISA  0.085000  0.086834  0.978886  0.3286  
  DK  0.194662  0.031952  6.092328  0.0000  

R-squared  0.998686     Mean 
dependent var  

 14.96179  

Adjusted R-squared  0.998650     S.D. dependent 
var  

 2.696541  

S.E. of regression  0.099084     Akaike info 
criterion  

 -1.755297  

Sum squared resid  2.464226     Schwarz 
criterion  

 -1.645434  

Log likelihood  235.3110     Hannan-Quinn 
criter  

 -1.711126  

F-statistic  27262.02    Durbin-Watson 
stat  

 0.730440  

Prob(F-statistic)  0.000000        
 DK probe. = 0.000 <0.05 DK is significant. Structural break has been corrected as shown in the 
graphic. 
Figure2. Structural Break (with Dummy Variable)  

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Our chart did not deviate outside the specified range. Structural break has been corrected.   
2.4. Pre-Testing and Evaluation of Results. Whether or not  the time series are stationary or not and 
the presence of a unit root were examined by the widely used Improved Dickey-Fuller (ADF) test, and 
the co-integration degree of the series was determined.  
2.5. Delay Length Analysis 
The results of the tests performed to determine the length of the delay to be used in the model are given 
in Table 3. The results symbolized with '' * '' related to testing show the appropriate lag length.  
 Table3. Determine the Lag Order Criteria  
  
VAR Lag Order Selection Criteria  
Endogenous variables: LOGINTERESTSA LOGBISTSA LOGCREDITSA LOGEXCHANGESA 
LOGM1SA LOGCPISA LOGIPISA   
Exogenous variables: C   
Sample: 1990M01 2011M11  
Included observations: 244  
 Lag  LogL  LR  FPE  AIC  SC  HQ  
0 -984.6632  NA    7.99e-06   8.128387   8.228716   8.168794  
1 1690.345   5174.606   3.59e-15  -13.39627  -12.59364  -13.07301  
2 1850.057   299.7883   1.45e-15  -14.30375   -12.79882*  -13.69765  
3 1953.843   188.8566   9.27e-16  -14.75281  -12.54559   -13.86386*  
4 2004.963   90.08695    9.15e-16*   -14.77018*  -11.86066  -13.59839  
5 2038.298   56.83496   1.05e-15  -14.64179  -11.02996  -13.18714  
6 2076.128   62.32582   1.16e-15  -14.55023  -10.23610  -12.81274  
7 2114.008   60.23538   1.29e-15  -14.45908  -9.442652  -12.43874  
8 2140.686   40.89105   1.58e-15  -14.27611  -8.557381  -11.97292  
9 2174.893   50.46986   1.83e-15  -14.15486  -7.733830  -11.56882  
10 2216.910   59.58147   2.01e-15  -14.09762  -6.974292  -11.22874  
11 2269.413   71.43814   2.03e-15  -14.12633  -6.300702  -10.97460  
12 2354.503   110.8971   1.59e-15  -14.42216  -5.894229  -10.98758  

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13 2411.837   71.43176   1.58e-15  -14.49047  -5.260235  -10.77304  
14 2469.657    68.72068*   1.58e-15  -14.56276  -4.630232  -10.56249  
15 2512.774   48.77166   1.81e-15  -14.51454  -3.879710  -10.23142  
 * 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          
 HQ: Hannan-Quinn information criterion         
In this context, the longest delay time in all periods is taken as 15 months. According to the test results, 
the optimal lag length in Likelihood Ratio Test (LR) is 14 months, the last prediction error (FPE) is 4 
months, Akaike Information Criterion (AIC) is 4 months, Schwarz Information Criterion is 2 months, 
and Hannan–Quinn information criterion (HQ) is 3 months. The appropriate lag length for the VAR 
model is set as 2 months, based on the Schwarz Information Criterion.    
2.6. Unit Root Analysis   
The results of the unit root analysis are given in the following table. In this study, the stationary model 
and the stationary in the model including both constant and trend will be examined. Money supply 
logarithm is taken, seasonally adjusted and identified as LOGM1SA. The results of the model with a 
constant for LOGM1SA variable are shown below.   
Table 4. Unit Root Test Results of Constant M1 Variable  
  

Null Hypothesis: LOGM1SA has a unit 
root   
Exogenous: Constant   
Lag Length: 2 (Automatic - based on SIC, 
maxlag=2)   

  

      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic   -4.518377    0.0002   
Testcriticalvalues:   1%level     -3.455786     
   5%level     -2.872630     
  10%level     -2.572754     

*MacKinnon (1996) one-sided p-values.      
 
According to the Τ statistics given by MacKinnon, 1%, 5%, 10% (-3455, -2872, -2572), as t value (-6737), 
whose significance levels are calculated, is high as the absolute value, ΔLOGM1SA has no unit root, and 
the series is stationary. The results of the model is located in constant and linear trend LOGM1SA.  
Table 5. Unit Root Test Results of Constant and Linear Trend M1 Variable 

Null Hypothesis: LOGM1SA has a unit root   
Exogenous: Constant, Linear Trend   
Lag Length: 2 (Automatic - based on SIC, 
maxlag=2)   

  

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      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic    0.745462    0.9997   
Test critical values:  1% level     -3.994310     
  5% level     -3.427476     
  10%level     -3.137059     

*MacKinnon (1996) one-sided p-values.      
 
According to MacKinnon τ statistics, 1%, 5%, 10% (-3994, -3427, -3137), the significance levels of t value 
(0745) are calculated. Since it is low as the absolute value, the LOGM1SA series has unit root; the series 
is not in stationary state. ΔLOGM1SA is produced to make the new series stable.  
Table 6. Unit Root Test Results First Degree of Difference Constant and Linear Trend M1 
Variable  
Null Hypothesis: D(LOGM1SA) has a unit root  
Exogenous: Constant, Linear Trend  
Lag Length: 1 (Automatic - based on SIC, maxlag=2)  

      t-Statistic   Prob.*  
Augmented Dickey-Fuller test 
statistic  

 -16.5269   
0.0000  

Test critical  1% level values:    -3.99431    

  5% level    -3.42748    
  10% level    -3.13706    

*MacKinnon (1996) one-sided p-values.  
According to the t statistics given by MacKinnon, 1%, 5%, 10% (-3994, -3427, -3137), the significance 
levels of t value (-16,526) is calculated. Since it is high as the absolute value, ΔLOGM1SA has no unit 
root series. They are stationary. Constant and linear trends LOGM1SA have been co integrated. The 
logarithm of the actual basic interest rate and the seasonally adjusted time series has been identified as 
LOGINTERESTSA. The results of the model are located in constant LOGINTERESTSA.  
 Table 7. Unit Root Test Results of Constant Interest Variable  

Null Hypothesis: LOGINTERESTSA has a unit 
root   
Exogenous: Constant   
Lag Length: 1 (Automatic - based on SIC, 
maxlag=2)   

  

      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic    0.247957    0.9751   
Test critical values:  1% level     -3.455685     
  5% level     -2.872586     
  10% level     -2.572730     

*MacKinnon (1996) one-sided p-values.      
  

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In accordance with MacKinnon τ statistics, 1%, 5%, 10% (-3.456; -2872; -2572), t value significance 
levels are calculated (0.247). It is low as the absolute value, so LOGINTERESTSA has the unit root; the 
series is not in a stationary state. ΔLOGINTERESTSA is designed to make the new series stable.   
 Table 8. Unit Root Test Results First Degree of Difference Constant Interest Variable  

Null Hypothesis: DLOGINTERESTSA has a 
unit root   
Exogenous: Constant   
Lag Length: 0 (Automatic - based on SIC, 
maxlag=2)   

  

      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic   -23.61517    0.0000   
Test critical values:  1% level     -3.455685     
  5% level     -2.872586     
  10% level     -2.572730     

*MacKinnon (1996) one-sided p-values.      
  
In accordance with the MacKinnon τ statistics, 1%, 5%, 10% (-3.456; -2872; -2572), as t value (-23 615), 
whose significance levels are calculated, is high as the absolute value, ΔLOGINTERESTSA has no unit 
root; the series is in stationary state. Constant and linear trend LOGINTERESTSA has been 
cointegrated. The results of the model is located in constant and trend LOGINTERESTSA.   
Table 9. Unit Root Test Results of Constant and Linear Trend Interest Variable   
  

Null Hypothesis: LOGINTERESTSA has a 
unit root   
Exogenous: Constant, Linear Trend   
Lag Length: 1 (Automatic - based on SIC, 
maxlag=2)   

  

      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic   -

2.200790    
0.4867   

Test critical values:  1% level     -3.994167     
  5% level     -3.427407     
  10% level     -3.137018     

*MacKinnon (1996) one-sided p-values.      
  
According to MacKinnon τ statistics, 1%, 5%, 10% (-3.994; -3427; -3137), t value (-2200), whose 
significance levels are calculated, is low as the absolute value, the LOGINTERESTSA has unit root; the 
series is not stationary. ΔLOGINTERESTSA is designed to make it stable. 
Table 10. Unit Root Test Results First Degree of Difference Constant and Linear Trend 
Interest Variable  
  

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Null Hypothesis: DLOGINTERESTSA has a unit 
root   
Exogenous: Constant, Linear Trend   
Lag Length: 0 (Automatic - based on SIC, 
maxlag=2)   

  

      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic   -23.79151    0.0000   
Test critical values:  1% level     -3.994167     
  5% level     -3.427407     
  10% level     -3.137018     

*MacKinnon (1996) one-sided p-values.      
  
According to MacKinnon τ statistics, 1%, 5%, 10% (-3.994; -3427; -3137), t value (-23 791), whose 
significance levels are calculated, is high as the absolute value, and thus, ΔLOGINTERESTSA has unit 
root and these series are stationary. Constant and linear trend LOGINTERESTSA has been 
cointegrated. The logarithm of the National 100 Index (closing price) and seasonally adjusted time 
series has been defined as the LOGBISTSA. The result of the model is located in constant LOGBISTSA  
 Table 11. Unit Root Test Results of Constant BIST Variable  
  
Null Hypothesis: LOGBISTSA has a unit root  
Exogenous: Constant  
Lag Length: 1 (Automatic - based on SIC, maxlag=2)  

      t-Statistic    Prob.*  
Augmented Dickey-Fuller test 
statistic  

 -
1.418092  

 
0.5733  

Test critical values:  1% level    -
3.455685  

  

  5% level    -
2.872586  

  

  10% level    -
2.572730  

  

*MacKinnon (1996) one-sided p-values.    
In accordance with t statistics given by MacKinnon, 1%, 5%, 10% (-3455, -2872, -2572), since t value (-
1418), whose significance levels are calculated, is low as the absolute value, LOGBISTSA has unit root; 
the series is not in stationary state. ΔLOGBISTSA is designed to make the new series stable.    
Table 12. Unit Root Tests Results the First Degree of Difference Constant And Linear 
Trend BIST Variable  

Null Hypothesis: DLOGBISTSA has a unit 
root   
Exogenous: Constant, Linear Trend   
Lag Length: 0 (Automatic - based on SIC, 
maxlag=2)   

  

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      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic   -12.52862    0.0000   
Test critical values:  1% level     -3.994167     
  5% level     -3.427407     
  10% level     -3.137018     

*MacKinnon (1996) one-sided p-values.      
  
According to MacKinnon τ statistics, 1%, 5%, 10% (-3456, -2872, -2572), as t value (-12 471), whose 
significance levels are calculated, is high as the absolute value, ΔLOGBISTSA has unit root, and the 
series is in stationary state. Since it as large as the absolute value, ΔLOGBISTSA has no unit root and 
the series is in stationary state. Constant and linear trend LOGBISTSA has been cointegrated. The 
results of the model are located in constant and trend LOGBISTSA.  
Table 13. Unit Root Test Results of Fixed BIST Variable  
  
Null Hypothesis: LOGBISTSA has a unit root  
Exogenous: Constant, Linear Trend  
Lag Length: 1 (Automatic - based on SIC, maxlag=2)  
      t-Statistic    Prob.*  
Augmented Dickey-Fuller test statistic  -1.121951   0.9223  
Test critical values:  1% level    -3.994167    
  5% level    -3.427407    
  10% level    -3.137018    
*MacKinnon (1996) one-sided p-values.    
  
In accordance with t statistics given by MacKinnon, 1%, 5%, 10% (-3994, -3427, -3137), the significance 
levels of t value (-1121) are calculated. It is low as the absolute value, LOGBISTSA has unit root, and the 
series is not in stationary state. ΔLOGBISTSA is designed to make the new series stable.  
  
Table 14. Unit Root Tests Results the First Degree of Difference Constant and Linear 
Trend BIST Variable   
  
Null Hypothesis: DLOGBISTSA has a unit root  
Exogenous: Constant, Linear Trend  
Lag Length: 0 (Automatic - based on SIC, maxlag=2)  

      t-Statistic    Prob.*  
Augmented Dickey-Fuller test 
statistic  

 -
12.52862  

 
0.0000  

Test critical values:  1% level    -
3.994167  

  

  5% level    -
3.427407  

  

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  10% level    -3.137018    
*MacKinnon (1996) one-sided p-values.    
  
MacKinnon τ statistics are given as 1%, 5%, 10% (-3994, -3427, -3137), and the significance levels of t 
value (-12 528) are calculated. Since it is high as the absolute value, ΔLOGBISTSA has unit root, and 
the series are in stationary state. Constant and linear trend LOGBISTSA has been cointegrated.  
The logarithm of the Purchase Price Dollar Currency exchange and the seasonally adjusted time series 
have  
been described as the seasonally adjustedLOGEXCHANGESA. The results of the model are presented 
in constant  
LOGEXCHANGESA.  
 Table 15. Unit Root Test Results of Constant Exchange Variable  
  

Null Hypothesis: LOGEXCHANGESA has a 
unit root   
Exogenous: Constant   
Lag Length: 1 (Automatic - based on SIC, 
maxlag=2)   

  

      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic   -3.573792    0.0069   
Test critical values:  1% level     -3.455685     
  5% level     -2.872586     
  10% level     -2.572730     

*MacKinnon (1996) one-sided p-values.      
  
According to t statistics given by MacKinnon, 1%, 5%, 10% (-3455, -2872, -2572), t value (-3573), whose 
significance levels are calculated, is high as the absolute value. LOGEXCHANGESA has no unit root, 
and the series is in stationary state.The results of the model are located in constant and trend 
LOGEXCHANGESA.  
Table 16. Unit Root Test Results of Constant and Linear Trend Exchange Variable  
  

Null Hypothesis: LOGEXCHANGESA has a 
unit root  Exogenous: Constant, Linear Trend   
Lag Length: 1 (Automatic - based on SIC, 
maxlag=2)   

  

      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic   -0.272673    0.9911   
Test critical values:  1% level     -3.994167     
  5% level     -3.427407     
  10% level     -3.137018     

*MacKinnon (1996) one-sided p-values.      

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In accordance with t statistics given by MacKinnon, 1%, 5%, 10% (-3994, -3427, -3137), the significance 
levels of t value (-0272) are calculated. As it is low as the absolute value, ΔLOGEXCHANGESA has unit 
root, and the series are not in stationary state. ΔLOGEXCHANGESA is produced to make the new series 
stable.  
Table 17. Unit Root Tests Results the First Degree of Difference Constant and Linear 
Trend Exchange Variable   
  

Null Hypothesis: DLOGEXCHANGESA has a 
unit root   
Exogenous: Constant, Linear Trend   
Lag Length: 0 (Automatic - based on SIC, 
maxlag=2)   

  

      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic   -10.56528    0.0000   
Test critical values:  1% level     -3.994167     
  5% level     -3.427407     
  10% level     -3.137018     

*MacKinnon (1996) one-sided p-values.      
  
In accordance with MacKinnon τ statistics, 1%, 5%, 10% (-3994, -3427, -3137), significance levels of t 
value (-10 565) are calculated. As it is high as the absolute value, ΔLOGEXCHANGESA has no unit root, 
the series is in stationary state. Constant and linear trend ΔLOGEXCHANGESA has been 
cointegrated.The logarithm of the Industrial Production Index and seasonally adjusted time series has 
been described as LOGIPISA., and the results of the model with a constant for LOGIPISA variable are 
presented below.  
 Table18. Unit Root Tests Results of Constant IPI Variable  
  

Null Hypothesis: LOGIPISA has a unit root   
Exogenous: Constant   
Lag Length: 1 (Automatic - based on SIC, 
maxlag=2)   

  

      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic   -2.336287    0.1614   
Test critical values:  1% level     -3.455685     
  5% level     -2.872586     
  10% level     -2.572730     

*MacKinnon (1996) one-sided p-values.      
  

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In accordance with t statistics given by MacKinnon, 1%, 5%, 10% (-3457, -2872, -2573), t value (-2336) 
significance levels are calculated. It is low as the absolute value, LOGIPISA has unit root, and the series 
is not in stationary state. ΔLOGIPISA is designed to make the new series stable.  
 Table 4. 19. Unit Root Tests Results the First Degree of Difference Constant IPI Variable  

Null Hypothesis: DLOGIPISA has a unit root   
Exogenous: Constant   
Lag Length: 0 (Automatic - based on SIC, 
maxlag=2)   

  

      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic   -24.30972    0.0000   
Test critical values:  1% level     -3.455685     
  5% level     -2.872586     
  10% level     -2.572730     

*MacKinnon (1996) one-sided p-values.      
  
According to MacKinnon τ statistics given as 1%, 5%, 10% (-3457, -2873, -2573), t value (-24 309) 
significance levels are calculated. Since it is low as the absolute ΔLOGIPISA has no unit root; the series 
is stationary. Constant and linear trend ΔLOGIPISA has been cointegrated. The results of the model are 
located in constant and trend LOGIPISA.  
  
Table 20. Unit Root Tests Results of Constant and Linear Trend IPI Variable  
  
Null Hypothesis: LOGIPISA has a unit root  
Exogenous: Constant, Linear Trend  
Lag Length: 1 (Automatic - based on SIC, maxlag=2)  
      t-Statistic    Prob.*  
Augmented Dickey-Fuller test statistic  -3.554144   0.0359  
Test critical values:  1% level    -3.994167    
  5% level    -3.427407    
  10% level    -3.137018    
*MacKinnon (1996) one-sided p-values.    
  
In accordance with t statistics given by MacKinnon, 1%, 5%, 10% (-3994, -3427, -3137), as t value (-
3554), whose significance level is calculated, is high as the absolute value LOGIPISA has no unit root. 
Constant and linear trend ΔLOGIPISA has been cointegrated. The logarithm of Banking Sector 
Domestic Credit Volume and the seasonally adjusted figure is described as LOGCREDITSA. The results 
of the model with a constant for LOGCREDITSA variable are displayed below.  
 
 
 
 

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 Table 4. 21. Unit Root Test Results of Constant Credit Varieble 
Null Hypothesis: LOGCREDITSA has a unit 
root   
Exogenous: Constant   
Lag Length: 1 (Automatic - based on SIC, 
maxlag=2)   

  

      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic   -4.549155    0.0002   
Test critical values:  1% level     -3.455685     
  5% level     -2.872586     
  10% level     -2.572730     

*MacKinnon (1996) one-sided p-values.      
  
According to Τ statistics given by MacKinnon, 1%, 5%, 10% (-3456, -2873, -2573), as t value (-4549), 
whose significance levels are calculated, is high as the absolute, LOGCREDITSA has no unit root and 
the series is in stationary state.  
 Table 4. 22. Unit Root Test Results of Fixed Constant and Linear Trend Credit Variable 
Null Hypothesis: LOGCREDITSA has a unit root  
Exogenous: Constant, Linear Trend  
Lag Length: 1 (Automatic - based on SIC, maxlag=2)  

      t-Statistic    Prob.*  
Augmented Dickey-Fuller test 
statistic  

 -0.691602   
0.9720  

Test critical values:  1% level    -3.994167    
  5% level    -3.427407    
  10% level    -3.137018    

*MacKinnon (1996) one-sided p-values.    
  
In accordance with t statistics given by MacKinnon, 1%, 5%, 10% (-3994, -3427, -3137), significance 
levels of t value (-0691) are calculated. It is low as the absolute value, so, LOGCREDITSA has unit root, 
the series is not stationary. ΔLOGCREDITSA is designed to make the new series stable.  
Table 23. Unit Root Test Results First Degree of Difference Constant and Linear Trend 
Credit Variable  

Null Hypothesis: DLOGCREDITSA has a 
unit root   
Exogenous: Constant, Linear Trend   
Lag Length: 2 (Automatic - based on SIC, 
maxlag=2)   

  

      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic   -6.175402    0.0000   

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Test critical values:  1% level     -
3.994453   

  

  5% level     -3.427546     
  10% level     -3.137100     

*MacKinnon (1996) one-sided p-values.      
 
In accordance with t statistics given by MacKinnon, 1%, 5%, 10% (-3996, -3428, -3137), as t value (-
6175), whose significance level is calculated, is high as the absolute value, ΔLOGCREDITSA has no unit 
root and the series is in stationary state. Constant and linear trend ΔLOGCREDITSA has been 
cointegrated. The logarithm of the Consumer Price Index and the seasonally adjusted figure has been 
identified as LOGCPISA. The model results for constant LOGCPISA variable are given below.  
 Table 4. 24. Unit Root Test Results of Constant CPI Variable  

Null Hypothesis: LOGCPISA has a unit root   
Exogenous: Constant   
Lag Length: 0 (Automatic - based on SIC, 
maxlag=2)   

  

      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic   -1.401741    0.5814   
Test critical values:  1% level     -3.455585     
  5% level     -2.872542     
  10% level     -2.572707     

*MacKinnon (1996) one-sided p-values.      
  
According to Τ statistics given by MacKinnon, 1%, 5%, 10% (-3455, -2872, -2572), as t value (-1401), 
whose significance levels are calculated, is low as the absolute value, LOGCPISA has unit root, the series 
is not stationary. ΔLOGCPISA is designed to make new series stable. 
Table 25. Unit Root Tests Results First Degree of Difference Constant CPI  
  

Null Hypothesis: DLOGTUFESA has a unit 
root   
Exogenous: Constant   
Lag Length: 0 (Automatic - based on SIC, 
maxlag=2)   

  

      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic   -15.94255    0.0000   
Test critical values:  1% level     -3.455685     
  5% level     -2.872586     
  10% level     -2.572730     

*MacKinnon (1996) one-sided p-values.      
 

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According to MacKinnon τ statistics, 1%, 5%, 10% (-3455, -2872, -2572), since t value (-15 942), whose 
significance levels are calculated, is high as the absolute value, ΔLOGCPISA has no unit root; the series 
is in stationary state. Constant and linear trend ΔLOGCPISA has been cointegrated. The results of the 
model are located in constant and trend LOGCPISA.  
 Table 4. 26. Unit Root Test Results of Constant and Linear Trend CPI Variable  

Null Hypothesis: LOGCPISA has a unit root   
Exogenous: Constant, Linear Trend   
Lag Length: 0 (Automatic - based on SIC, 
maxlag=2)   

  

      t-Statistic     Prob.*   
Augmented Dickey-Fuller test statistic   -2.012527    0.5913   
Test critical values:  1% level     -

3.994026   
  

  5% level     -3.427339     
  10% level     -3.136978     

*MacKinnon (1996) one-sided p-values.      
 
In accordance with t statistics given by MacKinnon, 1%, 5%, 10% (-3994, -3427, -3136), as t value (-
2012), whose significance levels are calculated, is low as the absolute value, LOGCPISA has unit root 
and the series is not in stationary state. ΔLOGCPISA is designed to make the new series stable.    
Table 27. Unit Root Tests Results the First Degree of Difference Constant and Linear 
Trend CPI Variable  
Null Hypothesis: DLOGCPISA has a unit root  
Exogenous: Constant, Linear Trend  
Lag Length: 0 (Automatic - based on SIC, maxlag=2)  

      t-Statistic    Prob.*  
Augmented Dickey-Fuller test 
statistic  

 -
15.93696  

 
0.0000  

Test critical values:  1% level    -
3.994167  

  

  5% level    -
3.427407  

  

  10% level    -3.137018    
*MacKinnon (1996) one-sided p-values.     
 According to MacKinnon τ statistics, 1%, 5%, 10% (-3994, -3427, -3137), since t value (-15 936), whose 
significance levels are calculated, is high as absolute value, ΔLOGCPISA has no unit root; the series is 
in stationary state. Constant and linear trend ΔLOGCPISA has been cointegrated. All the variables have 
constant and linear trends and they are in stationary state.    
2.7. Sorting Variable   

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In light of all this information; the variables are sorted according to how they are influenced by the 
monetary transmission mechanism as M1SA, INTERESTSA, BISTSA, CREDITSA, EXCHANGESA, 
CPISA and IPISA.    
3. Conclusion   
 The model results suggest the exchange rate channel in Turkey have influence over the general level of 
prices. However, it does not significantly influence production levels. As mostly imported raw materials 
are used in the production phase in Turkey, exchange rate shocks adversely affect the real economy. On 
the other hand, conditions are reversed for exports. The price level of final goods increases drastically 
due to exchange rate shocks, which has a negative impact on the balance sheet. Turkey has been 
struggling with hyperinflation for decades. Therefore, the financial sector has been returning to the 
market with short term contracts. Thus, the effect of changes in monetary policy causes the goods and 
services sector to emerge for a short duration.   
 The development of stock prices is negatively affected due to the continuing development on the capital 
market operations. Results include no findings over stock prices and credit channel. On the other hand, 
short term debt financed the budget deficit and raised the real interest rates in the 1990s. This led 
accelerated investment banks to purchase government bonds rather than funding the open market. 
Thus, the banking sector entered into the crisis by not fulfilling the most basic function of financial 
intermediation. The restructuring process that was executed in the aftermath of the 2001 crisis was 
considered to be an obstacle to the operations of credit channels. Comments in the financial system in 
Turkish economy and the analysis of applications made to create Var models support the literature. 
Traditional interest rates work effectively in Turkey. The fact that the exchange rate channel has no 
considerable effect on the overall outcome, it is observed that it significantly affects the general price 
level. In addition, stock prices and the credit channel are deemed to be working ineffectively.   
References   

SAHN, Byung Chan; “Monetary Policy and the Determination of the Interest Rate and Exchange Rate 
in a Small Open Economy with Increasing Capital Mobility”, Federal Reserve Bank of St.Louis 
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ANGELONI, IGNAZIO, Anil K. KASHYAP, Benoit MOJON ve Daniele TERLIZZESE, (2003) “Monetary 
Transmission in the Euro Area: Does the Interest Rate Channel Explainit All?”, NBER Working 
Paper, No: 9984, p.1 – 41.  

ARCANGELIS, Giuseppe De and Di Giorgio GIORGIO; “Monetary Policy Shocks and Transmission in 
Italy: A VAR Analysis”, 1999.http://www.econ.upf.edu/docs/papers/downloads/446.pdf  

BERNANKE, Ben S. and Alan S. BLINDER; “The Federal Funds Rate and The Channels of Monetary 
Policy”, American Economic Review, 82 (4), 1992, p. 901-921.  

CHAROENSEANG, June and Pornkamol MANAKIT; ‘‘Thai Monetary Policy Transmission In An 
Inflation Targeting Era’’, Journal of Asian Economics, 18, 2007, p. 144 –157.  

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FERREIRA, Cândida; ‘‘The Bank Lending Channel Transmission Of Monetary Policy In The Emu: A 
Case Study of Portugal’’ The European Journal of Finance, 2007, vol. 13, no. 2, p.181-193.  

FRIEDMAN, Milton; ‘‘John Maynard Keynes’’ Economic Quarterly, Federal Reserve Bank of 
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FRIEDMAN, Milton and Anna J. SCHWARTZ ‘‘Monetary Trends in The United States and The United 
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HOLTEMOLLER, Oliver; “Identifying a Credit Channel of Monetary Policy Transmission and 
Empirical Evidence for Germany”, 2002.  

ITURRIAGA, Felix. J. LOPEZ; “More on the credit channel of monetary policy transmission: an 
international comparison”, Applied Financial Economics, Vol 10, 2000, p.423-434.  

MEHROTRA, Aaron N.; (2007), “Exchange and Interest Rate Channels During a Deflationary Era – 
Evidence From Japan Hong Kong and China”, Journal of Comparative Economics, 35, p. 188-
210.  

PAPADAMOU, Stephanos and Georgios OIKONOMOU; ‘‘The Monetary Transmission Mechanism: 
Evidence from Eight Economies in Transition’’, International Economic Journal, 21, (4), 2007.  

YUE YI Ding and Shuang–Hong ZHOU (2007), ‘‘Empirical Analysis of Monetary Policy Transmission’’, 
Chinese Business Review, 6, (3), p.6 13. 

 

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