The Turkish economy has experienced high and persistent inflation rates in the last two decades. This inflation has persisted despite many unsuccessful stabilization policies, which have caused volatility in macro-economic indicators. The main aim of this paper is to analyze the impact of velocity on inflationary trend in Turkey over the period between 1996 and 2001. We assumed that there is a direct relationship between the two factors. However, velocity is not the major cause of inflation. The integration and co-integration tests have been adopted on monthly time series data to test the validity of the model by adding some control variables. Results show that velocity has a weak and negative effect on the inflationary growth of Turkey during this period. The effects of other control variables on inflation growth have also been tested. Some aspects of this linear relationship have been obtained by Granger’s Causality Test.Velocity, Co-Integration Tests, Error Correction Mechanism, Granger’s Test

Ozturk, Ilhan

English

Research Papers in Economics

VELOCITY EFFECT ON INFLATIONARY GROWTH OF TURKEY:
EVIDENCE FROM CO-INTEGRATION ANALYSIS
AND GRANGER’S CAUSALITY TEST
Ilhan Ozturk
(Cag University, Faculty of Business and Economics, Department of Economics, Adana –
Merin Karayolu uzeri, 33800 Yenice/Mersin, TYRKEY, Phone: +00 90 324 6514800, e-mail:
ilhanozturk@yahoo.com)
1. Introductýon
Turkey, a developing country, has experienced recently high inflation rates. The trend of
inflation has caused other macro economic indicators to be unstable over the years. This made
inflationary indicators the most important and leading economic indicator in the country. Among
the major causes of inflation are persistent public sector deficits, high input prices due to rapid
depreciation of the Turkish Lira (TL) and persistent inflationary expectations of economic
agents (Dibooglu and Kibritcioglu, 2001: 2). Many programs based their anticipations on
inflationary trend. Currently, another economic program is in action and trying to control and
reduce inflation and provide the stability for other macroeconomic indicators on the way to
European Union, as promised to the International Monetary Fund. Among other important
indicators are exchange rates and interest rates, which are highly important to both domestic
and foreign indicators.
.00
.02
.04
.06
.08
.10
1996 1997 1998 1999 2000 2001
DCPI
0
400000
800000
1200000
1600000
2000000
1996 1997 1998 1999 2000 2001
DOLARORG
0
5
10
15
20
25
30
1996 1997 1998 1999 2000 2001
INTEREST
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
1996 1997 1998 1999 2000 2001
VELOCITY
Figure 1. Trend of CPI, TL/$ Rates, Interest Rates and VelocityVolume 50, No.1 51
constant and LogCPI terms respectively are significant at 0.01, therefore LogCPI is assumed to
be stationary in level showing no unit root. The percentage values show the level of a at which
the series are significant under normal distribution. So, all the series seem stationary in levels
including both constant and trend terms. However, they are in different lags.
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
1996 1997 1998 1999 2000 2001
LCPIORG
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
1996 1997 1998 1999 2000 2001
LDOLARORG
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1996 1997 1998 1999 2000 2001
LINTERES
13.5
14.0
14.5
15.0
15.5
16.0
16.5
17.0
1996 1997 1998 1999 2000 2001
LGDP
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1996 1997 1998 1999 2000 2001
LVELOCIT
Figure 2. Logarithm Trend of Inflation Rates and VelocityTHE INDIAN ECONOMIC JOURNAL 52
The Engel Granger (EG) static long run regression by Ordinary Least Squares (OLS) is
estimated to see if the residuals are stationary which would be sufficient enough to see the
existence of co-integration among variables in a long run regression [Gujarati, 1995: 726-729]:
LogCPI = -0.34 + 0.124(LogDollar) – 0.009(LogVelocity) – 0.001(LogInterest)+
                 (-3.53)           (2.39)                     (-0.37)          (-0.15)
0.030(LogGDP) – 0.030(LogDollar-1) - 0.097(LogDollar-2) – 0.061(LogVelocity-1) +
       (1.08)    (-0.39)  (-1.66)    (-2.51)
0.033(LogVelocity-2) + 0.0007(LogInterest-1) + 0.009(LogInterest-2) + 0.056(LogGDP-1) +
(1.30) (0.08) (1.11) (1.84)
0.007(LogGDP-2) + 1.353(LogCPI--1) – 0.448(LogCPI-2)
       (0.26)    (11.63) (-4.03)
R
2= 0.99 Adj. R
2= 0.99 DW = 1.86 c
2= 32.5 (Prob= 0.25)
ADF = - 7.59CV= -3.53 SER = 0.01 c
2Serial Corr = 2.56 (Prob= 0.27)
(t statistic for each parameter is given in the parentheses)
The model results show that velocity has a weak and negative effect on CPI which is not
significant, but 1 lagged velocity is significant at 0.01 and its elasticity coefficient is –0.061.
TL/$ rates are significant in level but are not significant at 1 and 2 lags. The residual based
ADF test statistic shows that the null of no-cointegration at 0.01 significant level is rejected.
Table 3. Residual Based ADF Test.
R
2 Adjusted
R
2 DW
Calculated
ADF
Residuals
Critical Values
1%              5%               10%
0.46 0.45 1.92 -7.59 -3.53         -2.90            -2.59
To confirm the uniqueness of the co-integrating vectors, the Maximum Likelihood test is
employed:
Table 4. Johansen Maximum Likelihood Procedure.
Null
Hypothesis
Alternative
Hypothesis l l Max. Critical
Value at 5% l l Trace Value
Critical
Value at
5%
r = 0 r = 1 37.02 33.46 83.96 68.52
r £ 1 r = 2 23.67 27.07 46.93 47.21
r £ 2 r = 3 15.69 20.97 23.26 29.68
r £ 3 r = 4 6.56 14.07 7.57 15.41
r £ 4 r = 5 1.01 3.76 1.01 3.76
The results confirm the unique co-integrating vector among the relevant variables. Since
the series are co-integrated, there is a long term equilibrium relationship between the series.
However, there might be disequilibrium in the short term.
Due to the static structure of the co-integrating regression, the estimates of the static co-
integrating regression are likely to create biased estimation [Ghatak and Fethi, 1998: 386-387].
To remedy this problem, lagged and differenced terms are suggested by some
econometricians [Banarjee and Hendry, 1986: 253-277]. Thus ECM is employed through
“general to specific” modelling approach for short run dynamics components [See Miller, 1991:
139-154].
The estimates of dynamic model of ECM for the short run period are as follows:
DLogCPI = 0.004 + 0.093(DLogDollar) – 0.032(DLogVelocity) – 0.0009(DLogInterest)+
       (0.72)           (1.98)                            (-1.48)                          (-0.14)
0.056(DLogGDP) – 0.087(DLogDollar-1) – 0.005(DLogDollar-2) – 0.074(DLogVelocity-1) +
        (2.30)                        (-1.55)                         (-0.09)  (-2.97)
0.007(DLogVelocity-2) + 0.009(DLogInterest-1) + 0.018(DLogInterest-2) + 0.052(DLogGDP-1)Volume 50, No.1 53
(0.28) (0.98)      (2.25)
(1.93)
+ 0.013(DLogGDP-2) +  1.481(DLogCPI--1) – 0.714(DLogCPI-2) – 1.000(et-1)
       (0.46) (7.49) (-5.17)  (-4.48)
R
2= 0.71 Adj. R
2= 0.62 DW = 1.99 c
2= 32.7 (Prob= 0.34)
SER = 0.01 c
2Serial Corr = 0.25 (Prob= 0.88)
(t statistic for each parameter is given in the parentheses)
The error correction term is negative and significant at 0.01% level. The magnitude of the
corresponding coefficient shows that all of any discrepancy between the actual and the long
run, or the equilibrium, value of LogInflation is eliminated or corrected each month. In other
words, inflation is adjusted to its equilibrium level and error correction term provides further
evidence that the variables in the equilibrium regression are co-integrated as ECM works
satisfactorily. The ECM model again shows that velocity has still negative effects on CPI in the
short run which is not significant at non-lagged value but significant at 1 lag at 0.01.
6. Granger Causalýty for Inflatýonary Process in Turkey
Regression analyses deals with dependency among variables, but it does not necessarily
imply causation (Gujarati, 2003: 696). The Granger Test for causality is such a technique
searching the direction of causality between variables. The Granger Test follows F distribution
having a null hypothesis stating no causality from X ﬁ Y.
The logarithmic variables used in this study have been tested for the causing of Inflationary
Growth, if any. The money supply component of M1 is added additionally to the test. Since
data is monthly, 12 lagged variables are prefered. The results founded in EVIEWS 4.0 are
given in Table 5:
Table 5. Granger Test for Causality for Inflationary Growth
  Null Hypothesis: Obs F-Statistic Probability
  LDOLAR does not Granger Cause LCPI 60  2.57469  0.01458
  LCPI does not Granger Cause LDOLAR  0.90631  0.55017
  LGDP does not Granger Cause LCPI 60  2.40590  0.02150
  LCPI does not Granger Cause LGDP  6.43638  7.3E-06
  LVELOCITY does not Granger Cause LCPI 60  2.63280  0.01277
  LCPI does not Granger Cause LVELOCITY  1.93128  0.06443
  LM1 does not Granger Cause LCPI 60  2.95017  0.00622
  LCPI does not Granger Cause LM1  1.55836  0.15020
  LINTEREST does not Granger Cause LCPI 59  1.87513  0.07452
  LCPI does not Granger Cause LINTEREST  0.78135  0.66520
  LGDP does not Granger Cause LDOLAR 60  1.21456  0.31210
  LDOLAR does not Granger Cause LGDP  3.96594  0.00070
  LVELOCITY does not Granger Cause LDOLAR 60  0.90072  0.55518
  LDOLAR does not Granger Cause LVELOCITY  1.36076  0.23072
  LM1 does not Granger Cause LDOLAR 60  1.36463  0.22883
  LDOLAR does not Granger Cause LM1  33.4092  1.4E-15
  LINTERES does not Granger Cause LDOLAR 59  7.27808  2.3E-06
  LDOLAR does not Granger Cause LINTEREST  5.92849  2.0E-05
  LVELOCITY does not Granger Cause LGDP 60  1.03091  0.44376
  LGDP does not Granger Cause LVELOCITY  1.70571  0.10793
  LM1 does not Granger Cause LGDP 60  1.09852  0.39149
  LGDP does not Granger Cause LM1  1.67498  0.11570
  LINTEREST does not Granger Cause LGDP 59  0.80232  0.64570
  LGDP does not Granger Cause LINTEREST  1.14808  0.35696
  LM1 does not Granger Cause LVELOCITY 60  2.19137  0.03531
  LVELOCITY does not Granger Cause LM1  1.41494  0.20552
  LINTEREST does not Granger Cause LVELOCITY 59  0.69288  0.74665
  LVELOCITY does not Granger Cause LINTEREST  0.68165  0.75674
  LINTEREST does not Granger Cause LM1 59  25.3953  1.7E-13
  LM1 does not Granger Cause LINTEREST  1.38256  0.22164
Table 5 shows that causality from TL/$ rates to Inflationary growth has been founded at a =
0.01 level. Two sided causality have been obtained between Velocity growth and inflationary
growth although a low correlation exists between the two. The probability values for F statisticsTHE INDIAN ECONOMIC JOURNAL 54
are given on the right side of Table 5. If these probability values are less than any a level, then
the hypothesis would be rejected at that level.
6. Conclusýon
The aim of the paper is to examine the impact of velocity growth on the inflationary trend of
Turkey between 1996 and 2001, using monthly data through co-integration and ECM
regression analyses. The uses of co-integration and ECM showed that velocity has inelastic
and negative long term effect in the EG sense on inflationary growth. In the short run dynamic
model, the effect of velocity on inflation is both inelastic and significant at 1 lagged value. The
Granger Test for causality proves the existence of two sided causality among Consumer Price
Index growth and velocity growth.
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Velocity Effect On Inflationary Growth of Turkey: Evidence From Co-integration Analysis and Granger's Causality Test

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