öRnek dönem-projesi-bitirme-tezi-ekonometri-mehmet-güçlü-tez-ödev

T.C.
EGE UNIVERSITY
THE FACULTY OF ECONOMICS AND ADMINISTRATIVE SCIENCES
DEPARTMENT OF ECONOMICS
ECONOMETRICS TERM PROJECT:
An Empirical Analysis of the Money Demand Function
in Turkey(1987-2010)
Burhanettin NOĞAY
Prof. Assist. Mehmet GÜÇLÜ
İZMİR - 2011
1

CONTENTS
1. INTRODUCTION
2. LITERATURE REVIEW
3. FORMULATION OF THE MODEL
4. DATA SOURCES AND DESCRIPTION
5. MODEL ESTIMATION AND HYPOTHESES TESTING
5.1 Hypotheses Testing
5.1.1 The t-Test : Significance Approach
5.2 Jarque-Bera Test : Normality Test
5.3 White’s Heteroscedasticity Test
5.4 Tests for Autocorrelation
5.4.1 Durbin-Watson d test : First-order autocorrelation AR(1)
5.5 Ramsey’s RESET Test : Test of Specification Errors
6. INTERPRETATION OF RESULTS
7. CONCLUSION
REFERENCES
APPENDICES
A. THE DATA SET
B. COMPUTER OUTPUTS
2

ABSTRACT
This paper empirically analyzes Turkey’s money demand function during 1987 to
2010 using annual data. This study also aims to analyze whether Keynesian demand for
money (Liquidity Preference Theory) is valid in Turkey. The money demand function was
estimated using the method which is Ordinary Least Squares. The findings show that money
demand function with narrow money aggregate M1, is in a stable relationship with income
but not interest rate.
NOTE: This study is just an exercise for the Ordinary Least Squares technique. Therefore we
did not use necessary and sufficient tests for money demand such as Unit Root Test,
Cointegration Tests..The results may have been insignificant or unstable because of this. As a
result, these findings are not fully reliable.
Keywords: Money Demand, Keynesian Money Demand, Turkey, OLS, GDP, Interest Rate
3

1. INTRODUCTION
The main purpose of this study is to analyze the main determinants of money demand
in Turkey under Keynes’s Liquidity Preference Theory for the period between 1987 and 2010
using annual data.
Economists have tried to explain money demand a variety of ways since 1990s. All the
theories developed aimed to answer a single question: Why do people keep money? (Keyder,
2008:347).
The money demand theories can be listed into two main groups.
1. The Monetarist Approach
2. The Neo-Keynesian Approach
In the first group, there are Classical Quantity Theory of Money(Fisher and Cambridge
Approaches) and Milton Friedman’s Modern Quantity Theory of Money.
Under the heading of the Neo-Keynesian Approach, there are Liquidity Preference
Theory, Portfolio Balance Approach, and Conditioning Theories of Wealth(Keyder,
2008:347).
We tried to analyze money demand function for Turkey under Keynes’s Liquidity
Preference Theory in this study. The main criticisms of Liquidity Preference Theory were
explained in this paper. There are no explanations of the other theories which are about
money demand because this is a subject outside of this framework.
In these days, interest rate, the role of Central Bank, bank credits, money balance and
like something these issues are being discussed too much in relation to current account deficit
in Turkey. All these discussions illustrate that Turkish economy is in need of stable and
well-defined money demand function. We would like to say that our study will play very
important role under this situation. However we cannot say that because as we mentioned
4

earlier there are no necessary and sufficient tests to determine a stable money demand
function.
2. LITERATURE REVIEW
In the following tables from Subramanian S. Sriram, there are many analyses about
money demand for variety countries.
5

Source: Sriram, Subramanian S. (1999), “Survey of Literature on Demand for Money:
Theoretical and Empirical Work with Special Reference to Error-Correction Models”.
On the other hand , in the following studies that represent analysis of money demand
with findings in Turkey.
Metin(1995) considered the narrow money demand M1 by using quarterly data for
1948:01-1987:04. Results show that Long-term money demand is in a positive relationship
with the high income elasticity on the other hand the money demand is in a negative
relationship with the elasticity of inflation.
Kogar(1995 tried to examine the long-term stability of money demand under
experienced periods of high inflation. Quarterly data that 1978:01-1990:04 were used for this
study. As a result of the study, there was found a significant relationship between M1, M2
and real income, inflation rate, exchange rate.
Civcir(2000) modeled the relationships between M2 money demand, real income,
interest rate and expectations exchange rate. Findings represent that real money demand is in
a positive relationship with real income and the real money demand is also in a negative
relationship with interest rate and expectation exchange rate.
14

Balaylar and Duygulu(2004) investigated whether there is a stable money demand
function for the period 1987-2000. Nominal money supply M2, nominal income, inflation,
weighted real effective exchange rate, three monthly interest rate on treasury bills and annual
interest rate on deposits were used in this study. With estimation results for all variables that
constitutes the money demand function, there is no a cointegration relationship between
variables.
Isık and Kadılar(2004) analyzed money demand for Turkey for the period between
1988:01 and 2004:01-quarterly data. As a result of the study, interest rate and exchange rate
elasticity of money demand(M1) was found low, but income elasticity was quite higher.
Asılı(2005) analyzed the stability of money demand for the period between 1987:01
and 2004:04. According to the results of the study, the money demand(M1) is stable. M2
money demand has been concluded unstable.
Agıralioglu(2006) aimed to estimate the factors which determine the money demand in
Turkey. Money demand as a determinants of real income, savings deposit interest rate,
rediscount rate, interbank interest rate, treasury bills, stock exchange, inflation rate and the
dollar variables has been taken into account. Variables of this data for 1989-2005 used. When
the results are examined, the most affecting variables for money demand, inflation, interest
rate and exchange rate that are observed.
Çatık(2006) investigated the stability relationship between real money demand and
real income, interest rate by using quarterly data for the period between 1988:01 and 2005:04.
From the results of the analysis of multivariable cointegration, there is a significant
relationship between variables in the long-run.
15

3. FORMULATION OF THE MODEL
In his famous 1936 book The General Theory of Employment, Interest, and Money,
Keynes developed a theory of money demand which he called liquidity preference theory.
Keynes abandoned the classical view that velocity was a constant, emphasized the
importance of interest rates. He postulated that there are three motives behind the demand for
money: the transactions motive, the precautionary motive, and the speculative
motive(Xueping, 2005).
Transactions motive: Keynes emphasized that this component of the demand for
money is determined primarily by the level of people’s transactions. The transactions demand
for money arises from the lack of synchronization of receipts and disbursements. In other
words, people aren’t likely to get paid at the exact instant you need to make a payment, so
between paychecks people keep some money around in order to buy stuff. Keynes believed
that these transactions were proportional to income, like the classical economists, he
considered the transactions component of the demand for money to be proportional to
income(Xueping, 2005).
Precautionary motive: Keynes also recognized people hold money not only to carry
out current transactions, but also as cushion against an unexpected need. Because people are
uncertain about the payments they might want, or have, to make. If people don’t have money
with which to pay, they will incur a loss. When you are holding precautionary money
balances, you can take advantages of the sale. Keynes believed that the amount of
precautionary money balances people want to hold is determined primarily by the level of
transactions that they expected to make in the future and that these transactions are
proportional to income. So he considered the demand for precautionary money balances to be
proportional to income (Xueping, 2005).
16

Speculative motive: The transactions motive and the precautionary motive for money
emphasized medium–of-exchange function of money, for each refers to the need to have
money on hand to make payments. Keynes agreed with the classical Cambridge economists
that money is a store of wealth and called this reason for holding money the speculative
motive. He also considered that wealth is tied to closely to income; the speculative
component of money demand would be related to income. Keynes believed that interest rates
have an important role to play in influencing the decisions regarding how much money to
hold as a store of wealth(Xueping, 2005).
All these motives can be seen on the following graph.
Source: http://www.lucidchart.com
17

In summary we can write the following:
(1) Transactions motive, (Mt =f(Y) )
(2) Precautionary motive, (Mp =f(Y) )
(3) Wealth(speculative) motive, (Ms =f(Y) )
In this way, MD = Mt + Mp + Ms
Combining all these factors, the total demand for real money balances can be
expressed as follows:
(M/P)d
= L(Y,r)
If we take short-term situation where prices do not change, there is no difference between real
money demand and nominal money demand. So that under constant price level in the short-
run, total money demand can be written as follows and it also gives us LM curve.
Md
= L(Y,r) (1)
where Md
represent nominal money supply, Y represents output; and r represents the
nominal interest rate. As we can see from equation (1), increases in output bring increases in
money demand, and increases in interest rates bring decreases in money demand.
We used the following model corresponding to equation(1) in order to conduct an
empirical analysis.
Mt
d
=β1 +β2Yt +β3Rt +Ut , where
Mt
d
= nominal money supply M1
Yt = nominal gross domestic product in terms of Turkish Lira
Rt = nominal interbank interest rate
Ut = disturbance term
t denotes year: 1987, 1988, 1990…
18

In summary we can write the following:
(1) Transactions motive, (Mt =f(Y) )
(2) Precautionary motive, (Mp =f(Y) )
(3) Wealth(speculative) motive, (Ms =f(Y) )
In this way, MD = Mt + Mp + Ms
Combining all these factors, the total demand for real money balances can be
expressed as follows:
(M/P)d
= L(Y,r)
If we take short-term situation where prices do not change, there is no difference between real
money demand and nominal money demand. So that under constant price level in the short-
run, total money demand can be written as follows and it also gives us LM curve.
Md
= L(Y,r) (1)
where Md
represent nominal money supply, Y represents output; and r represents the
nominal interest rate. As we can see from equation (1), increases in output bring increases in
money demand, and increases in interest rates bring decreases in money demand.
We used the following model corresponding to equation(1) in order to conduct an
empirical analysis.
Mt
d
=β1 +β2Yt +β3Rt +Ut , where
Mt
d
= nominal money supply M1
Yt = nominal gross domestic product in terms of Turkish Lira
Rt = nominal interbank interest rate
Ut = disturbance term
t denotes year: 1987, 1988, 1990…
19

According to the Keynes’s Liquidity Preference Theory income has a positive impact
on money demand so we expect the coefficient β2 to has a positive sign. On the other hand
interest rate has a negative impact on money demand so we expect the coefficient β3 to has a
negative sign.
4. DATA SOURCES AND DESCRIPTION
We used three type of data totally in this analysis. The data on monetary aggregate for
narrow money supply M1 for the period 1987-2010 has been taken from The Central Bank of
The Republic of Turkey(TCMB) Database. The data for the other variables which are gross
domestic product and interbank interest rate has been taken from International Financial
Statistics(IFS) Online Database. And all these data is available in the part “A” of the
Appendices. The data set including money supply M1, gross domestic product and interbank
interest rate. The ranges and original resources of variables are the followings:
M1t = M1 is a measure of total money supply. It consists of paper currency and coins, plus
publicly held demand deposits.
Source: The Central Bank of The Republic of Turkey(TCMB) Database where
http://www.evds.tcmb.gov.tr
Yt = GDP(Gross Domestic Product) in annual nominal value by Turkish Lira.
Source: International Financial Statistics(IFS) Online Database where
http://www.imfstatistics.org/imf
Rt=Nominal interbank interest rate(%)
Source: International Financial Statistics(IFS) Online Data Base where
20

Statistical Characteristics of Data
0
20000
40000
60000
80000
100000
120000
140000
88 90 92 94 96 98 00 02 04 06 08 10
M1
0
200000
400000
600000
800000
1000000
1200000
88 90 92 94 96 98 00 02 04 06 08 10
Y
0
20
40
60
80
100
120
140
88 90 92 94 96 98 00 02 04 06 08 10
R
21

Descriptive Statistics
5. MODEL ESTIMATION AND HYPOTHESES TESTING
We used the T-test whether our estimated coefficients are significance.
Note: The computer output of this regression is given in Appendices, part B
For all the following relevant tests we selected α=0.05 (5%) as a significance level.
5.1.1 The t-Test:
M
^
t
d
= - 5067,693 + 0,102Yt + 7,630Rt
Se (8644,726) (0,009) (115,207)
tstatistics (-0,586) (10,789) (0,066)
p (0,564) (0,000) (0,947)
22
M1 GDP R
Mean 26472.76 Mean 302684.7 Mean 51.30225
Median 3621.835 Median 87399.55 Median 54.31400
Maximum 135191.0 Maximum 1105100. Maximum 136.4710
Minimum 8.629000 Minimum 74.41600 Minimum 5.813000
Std. Dev. 40074.75 Std. Dev. 377933.3 Std. Dev. 31.28796
Skewness 1.413710 Skewness 0.876085 Skewness 0.544789
Kurtosis 3.738000 Kurtosis 2.241365 Kurtosis 3.384346
Jarque-Bera 8.538943 Jarque-Bera 3.645628 Jarque-Bera 1.334902
Probability 0.013989 Probability 0.161570 Probability 0.513015
Sum 635346.1 Sum 7264432. Sum 1231.254
Sum Sq. Dev. 3.69E+10 Sum Sq. Dev. 3.29E+12 Sum Sq. Dev. 22515.54
Observations 24 Observations 24 Observations 24

R2
= 0,932922
DW-statistic = 0,540343
n =24
where Se, tstatistic, and p represent standard deviations of intercept coefficient ( 1
ˆβ ) and slope
coefficients( , ), t-value and p- value respectively. 2
R denotes the coefficient of
determination.
a) For : = 0
tstatistic = -0,586 tcritical(0,05;21) = 2,080
tcritical |t˃ statistic|
2,080 |-0,586|˃
We don’t reject the null hypothesis. It means that 1
ˆβ - the intercept coefficient is statistically
insignificant.
b) For : = 0
: 0
tstatistic = 10,789 tcritical(0,05;21) = 2,080
23

10,789 2,080˃
We reject the null hypothesis. It means that the slope coefficient is statistically significant.
c) For : = 0
: 0
tstatistic = 0,066 tcritical(0,05;21) = 2,080
0,066 2,080˂
Since computed t value cannot exceed t-critical value we don’t reject the null hypothesis. It
means that the slope coefficient ( ) is statistically insignificant.
5.2 Jarque-Bera Test
We used Jarque-Beta Test to test whether the residuals of the estimated model are
normally distributed or not.
: Residuals are normally distributed
: Residuals are not normally distributed
JB critical (0,05; 2) = 5,991
JB statistic = 1,377
24

1,377 5,991˂
We don’t reject the null hypothesis. It means that the residuals of the estimated model are
normally distributed.
5.3 White’s Heteroscedasticity Test
To check whether the residuals of the regression are homoscedastic or heteroscedastic
we applied White’s Heteroscedasticity test (no cross term). The results are as follows:
Û2
t = 44092461 + 213,8251Yt + 4,092Y2
t -270395,8Rt -295,5394R2
t
R2
= 0,301
(n ) = 7,231
df = number of regressors (excluding the constant term) in the auxiliary regression.
: There is no heteroscedasticity
: There is a heteroscedasticity
(0,05; 4) = 9,487
= 7,231
2
critical
χ ˃
2
statistic
χ
9,487 7,231˃
Since the critical chi-square value at 5% level of significance is larger than the computed
25

chi-square value , we don’t reject the null hypothesis and conclude that there is
homoscedasticity.
5.4 Tests for Autocorrelation
5.4.1 Durbin-Watson d test
We use Durbin-Watson d test to examine the data for a first-order autocorrelation. The
results are given below:
M
^
t
d
= - 5067,693 + 0,102Yt + 7,630Rt
H0: No positive autocorrelation
H0
*
: No negative autocorrelation
n= 24 k’=2 dL = 1,188 dU = 1,546
Since 0 <DW = 0,540 < dL (=1,188), we reject H0. There is a first-order autocorrelation.
(Positive autocorrelation)
To solve the problem of first-order autocorrelation we use the adjusted regression. The
results obtained are as follows:
M
^
t
d
= - 9047,468 + 0,117Yt + 9,538Rt
26

H0: No positive autocorrelation
H0
*
: No negative autocorrelation
n=23 k’=2 dL = 1,168 dU = 1,543
27

dU < DW < 2 so do not reject H0 or H0
*
and conclude that there is no first order
autocorrelation.
5.4 Ramsey’s RESET Test
To find out whether our model was correctly specified or not, we use the Ramsey’s
Reset test. The results of the test are followings:
M
^
t
d
= 749,7348 + 0,027Yt - 8,371Rt +7,540(M
^
t
d
)2
+ 9,896(M
^
t
d
)3
R2
= 0,984294 F-statistic = 31,07
H0= The model is correctly specified
H1= The model is not correctly specified
Fstatistic = 31,07
Fcritical = F2,19;0,05) = 3,52
Fstatistic ˃ Fcritical
31,07 3,52˃
Since F statistic exceeds Fcritical we reject the null hypothesis and conclude that the model is not
correctly specified.
6. INTERPRETATION OF THE RESULTS
We estimated three coefficients for the money demand function in Turkey for the
period between 1987 and 2010. The coefficients , and give autonomous money
demand, income and interest rate elasticity, respectively. The autonomous money demand (
28

) is ≈ - 5067,693 but according to the significance t-test result, the coefficient is
statistically insignificant. The income coefficient( ) is 0,102, meaning that holding other
variables constant, if income increases by one unit mean money demand goes up by about
0,102 (Turkish Lira). This coefficient was tested by t-test significance approach and was found
statistically significant. We also estimated interest rate coefficient as 7,630. However according to the
results of the t-test, the was found statistically insignificant.
Some of estimated the regressors have not signs which are in compliance with priori
expectations from Liquidity Preference Theory, that’s interest and autonomous money
demand have a positive, negative sign respectively in this study. We think that can
be done because of no cointegration test in our model.
7. CONCLUSION
Regression of one time series variable on one or more time series variables often can
give nonsensical or spurious results. This phenomenon is known as spurious regression. One
way to guard against it is to find out if the time series are cointegrated (Gujarati,2004). Most
likely, this study may be faced the problem of spurious regression.
At the beginning of this study, we were expecting the coefficients of money demand
function that autonomous money demand coefficient was expected as positive, for the
29

coefficient of interest rate was expected as positive based on Keynes’s Liquidity Preference
Theory. As a result of the analysis, however the coefficient of interest found positive and
statistically insignificant. Similarly, autonomous money demand coefficient was negative
instead of positive and statistically insignificant. Additionally the model is not correctly
specified.
Generally all studies in the literature part and in many similar studies which are about
money demand function, Structural Breaks, Unit Root Tests, Cointegration Tests are used.
All these tests were not used in this analysis. Therefore the coefficients of autonomous
money demand and interest rate may have found as negative and positive respectively both
are also statistically insignificant.
We would like to state once again, this study is just an exercise for Ordinary Least
Square (OLS) method. The results of this study may not be reliable because of this reason.
In this study, we tried to analyze the money demand in Turkey for the period between
1987 and 2010. The results of this analysis are not suitable to main criticisms of Liquidity
Preference Theory.
30

REFERENCES
Elma, Çiğdem A.(2008), "Yapısal Kırılmalar Altında Birim Kök Testleri ve Eşbütünleşme
Analizi : Para Talebi İstikrarı", Ankara: Yüksek Lisans Tezi.
Eruygur, H. Ozan(2010), Lecture Notes Ankara: Gazi University.
E-Views 5 Help Menü, Users Guide.
Gujarati, Damodar N.(2004), "Basic Econometrics", The McGraw-Hill Companies.
Keyder, Nur(2008), "Para, Teori, Politika, Uygulama", Ankara: Seçkin Yayıncılık ve
Dağıtım, pp. 347-360.
Sriram, Subramanian S. (1999), "Survey of Literature on Demand for Money: Theoretical
and Empirical Work with Special Reference to Error-Correction Models", IMF Research
Depermant, IMF Working Paper.
Xueping, Liang(2005), Lecture Notes on the Internet,
http://web.cenet.org.cn/upfile/56317.pdf
Internet Sources:
http://www.evds.tcmb.gov.tr
http://web.cenet.org.cn
http://www.lucidchart.com
31

APPENDICES
A. THE DATA SET
obs M1 Y R
1987 8.629 74.416 39.318
1988 11.312 129.22 60.623
1989 19.558 227.33 40.663
1990 31.398 393.06 51.905
1991 46.793 630.12 72.745
1992 78.341 1093.38 65.354
1993 129.087 1981.86 62.83
1994 230.847 3868.43 136.471
1995 388.185 7762.46 72.3
1996 896.855 14772.1 76.24
1997 1581.21 28835.9 70.323
1998 2562.48 70203.1 74.597
1999 4681.19 104596 73.528
2000 7549.24 166658 56.723
2001 11368.8 240224 91.954
2002 15827.6 350476 49.505
2003 23014.3 454781 36.157
2004 28793.4 559033 21.415
2005 61991.2 648932 14.729
2006 71771 758391 15.59
2007 76350.7 843178 17.238
2008 85476 950534 15.995
2009 107347 952558 9.238
2010 135191 1105100 5.813
B. COMPUTER OUTPUTS
5.1.1 The t-Test : Significance Approach
Dependent Variable: M1
Method: Least Squares
Date: 05/15/11 Time: 21:47
32

Sample: 1987 2010
Included observations: 24
Variable Coefficient Std. Error t-Statistic Prob.
Y 0.102909 0.009538 10.78972 0.0000
R 7.630021 115.2078 0.066228 0.9478
C -5067.693 8644.726 -0.586218 0.5640
R-squared 0.932922 Mean dependent var 26472.76
Adjusted R-squared 0.926534 S.D. dependent var 40074.75
S.E. of regression 10862.13 Akaike info criterion 21.54042
Sum squared resid 2.48E+09 Schwarz criterion 21.68768
Log likelihood -255.4850 F-statistic 146.0344
Durbin-Watson stat 0.540343 Prob(F-statistic) 0.000000
33

5.2 Jarque-Bera Test : Normality Test
0
2
4
6
8
10
12
14
-20000 0 20000
Series: RESID
Sample 1987 2010
Observations 24
Mean -2.43e-12
Median 3502.013
Maximum 26489.48
Minimum -23831.89
Std. Dev. 10379.12
Skewness -0.124904
Kurtosis 4.146630
Jarque-Bera 1.377166
Probability 0.502287
5.3 White’s Heteroscedasticity Test(no cross term)
White Heteroskedasticity Test:
F-statistic 2.048495 Probability 0.128096
Obs*R-squared 7.231583 Probability 0.124145
Test Equation:
Dependent Variable: RESID^2
Date: 05/15/11 Time: 23:44
Sample: 1987 2010
C 44092461 2.85E+08 0.154525 0.8788
Y 213.8251 394.3734 0.542190 0.5940
Y^2 4.09E-05 0.000384 0.106618 0.9162
R -270395.8 7004257. -0.038604 0.9696
R^2 -295.5394 41922.90 -0.007050 0.9944
R-squared 0.301316 Mean dependent var 1.03E+08
Adjusted R-squared 0.154225 S.D. dependent var 1.87E+08
S.E. of regression 1.72E+08 Akaike info criterion 40.94741
5.4.1 Durbin-Watson d test : First-order autocorrelation ( AR(1))
34

Date: 05/16/11 Time: 00:59
Sample (adjusted): 1988 2010
Included observations: 23 after adjustments
Convergence achieved after 34 iterations
Y 0.117573 0.022394 5.250297 0.0000
R 9.538008 74.98593 0.127197 0.9001
C -9047.468 14208.93 -0.636745 0.5319
AR(1) 0.797819 0.214066 3.726976 0.0014
Inverted AR Roots .80
5.5 Ramsey’s RESET Test : Test of Specification Errors
Ramsey RESET Test:
F-statistic 31.07399 Probability 0.000001
Log likelihood ratio 34.84405 Probability 0.000000
Test Equation:
Date: 05/16/11 Time: 01:28
Sample: 1987 2010
Y 0.027278 0.020850 1.308326 0.2064
R -8.371409 61.14863 -0.136903 0.8925
C 749.7348 4526.520 0.165632 0.8702
FITTED^2 7.54E-06 5.99E-06 1.258220 0.2236
FITTED^3 9.90E-12 4.08E-11 0.242758 0.8108
35

36

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