Download The stability of money demand: Evidence from Turkey

The stability of money demand: Evidence from Turkey
PhD Chaido Dritsaki
Technological Institute of Western Macedonia, Greece
Department of Financial Applications
e-mail: [email protected]
and
PhD Melina Dritsaki
Brunel University
West London
e-mail: [email protected]
Abstract
Demand for money is an important macroeconomic relationship. Its stability
has implications for the choice of monetary policy targets. The current study
examines the stability of money demand function in Turkey from January 1989 to
May 2010. More specifically, it estimates the demand for narrow money in Turkey
and evaluates its robustness and stability. Considering the economic reforms and
financial crises in Turkey, it is found that there exists a well-determined instability for
money demand and its dynamics which is adequately captured by cointegration and
error correction models. Finally, the conclusions from the estimation of the impulse
response functions show that interest rate causes the largest shift in money demand as
well as in the industrial production.
Keywords: Demand for money, Monetary policy, Cointegration, Vector Error
Correction Model, Stability, Impulse response function.
JEL: C22, C32, E41, E52
1. Introduction
Demand for money investigates what motivates people to hold money
balances. Deducing from the estimations of money demand equations, the monetary
authority can decide which monetary policies are better to implement under the
current economic conditions. A stable demand function for money has long been
perceived as a prerequisite for the use of monetary aggregates in the conduct of policy
(Goldfeld and Sichel, 1990).
The effectiveness and success of a monetary programme crucially depends on
a stable money demand function. The stable money demand function ensures that the
money supply would have predictable impacts on other economic variables such as
inflation, interest rates, national income, and private investments. Therefore, the
stability issue in money demand function becomes an interesting research area for
researchers to test the effectiveness of a given monetary programme (Halicioglu and
Ugur, 2005).
Turkey has historically suffered a number of economic problems. Inflation
was high and persistent, the use of foreign currency, was widespread and the banking
sector was an oligopoly and domestic financial markets were shallow and volatile.
These problems led to a financial crisis in 2001. This crisis provided the impetus for a
number of economic reforms and the central bank independence was increased.
An authoritative and independent Monetary Policy Committee was created
concomitant with the increased role given to monetary policy. The fixed exchange
rate was abandoned and monetary policy adopted an inflation target. These reforms
were followed by a dramatic drop in the inflation rate (Butkiewicz and Ozdogan
2008).
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Turkey, as a developing country, has undertaken a number of stability
programmes since 1970s. Under the ongoing stability programme, the Central Bank
of Turkey (CBT) has been following a base-money targeting strategy. Τhe necessary
condition for effective monetary aggregate targeting is the existence of a stable longrun and short-run relationship between the monetary aggregate and the decreasing of
inflation.
The issue of stability on money demand has been examined thoroughly in the
last two decades using econometric methodologies, allowing scientists to have a more
in-depth examination on the stability concept which happens to be a long term
phenomenon.
The objective of this paper is to estimate a long-run version of money demand
in Turkey through cointegration tests and over the period 1989:M1 until 2010:M5.
To achieve this objective, the paper is organised as follows. Economic theory
for money demand is discussed in section 2. Section 3 provides a brief review of the
literature on stability of the money demand functions for Turkey. Section 4 deals with
data analysis. Section 5 illustrates the methodology used. Section 6 provides the
results. Finally, the concluding remarks are contained in section 7.
2.
Economic theory on money demand
In the classical theory money was held for transaction purposes or as a
medium of exchange. Money supply is defined as the sum of notes and coins, and the
demand deposits. The quantity theory of money is based on the assumptions that
money supply is exogenous and the income velocity of money is stable. If the velocity
is stable, then the demand for money is stable. Hence, there is a tight link between the
amount of money and the level of nominal income. In addition, this theory postulates
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that the economy moves to a long run full-employment equilibrium. In the long run,
the price level depends upon the quantity of money in the economy.
Monetarist approach to the quantity theory also assumes that the money
supply is exogenous and income velocity of money is stable. However, this approach
differs from the conventional quantity theory in explaining the link between the
money supply and the level of income. Monetarists postulate a direct transmission
mechanism from monetary to real sector through the real balance effect.
Friedman's quantity theory postulates that in the long run the income elasticity
of money demand is unity and velocity of money is constant; whereas Tobin's
transactions demand theory postulates that the income elasticity is 0.5 and velocity is
not constant. The theories on the value of long run income elasticity of money
demand are totally different as a result of their postulates about the money demand.
The main argument against conventional quantity theory is that the transaction
demand for money does not only have a response to the transaction needs, but also
has a response to the physiological factors (such as expectations), institutional factors
(such as credit facilities), and to any stochastic shock in the economy (Aysu, Insel
1997).
Keynes (1936) developed the liquidity preference theory which explicitly
highlights the transaction, precautionary and speculative motives for holding money.
In the Keynesian approach there is a link between the quantity of money and the level
of income in an economy, but this approach does not postulate that economy moves to
a long run full-employment equilibrium. It is assumed that the interest rate has an
important effect on the money demand and the income velocity of money is not
stable. Keynesians claim that there is an indirect transmission mechanism that works
through the interest rate effect on investment, and through the multiplier effect on the
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real sector of the economy. In a theoretical framework if (i) the economy does not
tend to move to a long run full employment level; or (ii) the income velocity of
money is not stable and does not depend upon the rate of interest; or (iii) there is not a
causality from money supply to either income or the price level, then it would be
possible to assume that money supply is exogenous. The changes in the postulates
such that instable velocity, financial innovations, expectations, and preferences lead to
the rejection of the quantity theory and result in the theory of an endogenous money
supply. The theory of endogeneity of money supply is based on political rather than
economic behaviour. That is, if there is an increase in demand for money, the central
bank cannot control the money supply, but can control the interest rate. Thus the
purpose of monetary policy is to target the rate of interest not the money supply. The
quantity of money is determined by the demand for it. Therefore, the rate of interest
has no effect on the amount of money individuals wish to hold, rather has an indirect
effect on demand due to changes in the level of income (Aysu Insel 1997).
Friedman (1956) opposed the Keynesian view that money does not matter and
presented the quantity theory as a theory of money demand. He modelled money as
abstract purchasing power (meaning that people hold it with the intention of using it
for upcoming purchases of goods and services) integrated in an asset and transactions
theory of money demand set within the context of neoclassical consumer and
producer behaviour microeconomic theory. Friedman argued that the velocity of
money is highly predictable and that the demand for money function is highly stable
and insensitive to interest rates. This implies that the quantity of money demanded can
be predicted accurately by the money demand function (Kumar, Webber and Fargher
2010).
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3.
Empirical studies in Turkey
This section includes the literature review on the empirical studies for Turkish
economy to modelling money demand.
Metin (1994) estimates M1 narrow money demand for the period 1948:Q11987:Q4. The results estimated confirm the existence of a long run money demand
relationship with relatively high positive income elasticity and also a negative
inflation elasticity as opportunity cost for the money demand equation.
In another study, Kogar (1995) tries to test whether there exists a stable long
run money demand function for Turkey and Israel, which experience high inflation
during the period under investigation. For the Turkish case, using quarterly data in the
period 1978:Q1- 1990:Q4, it is found that there exists a long run relationship between
real money (M1 and M2) demand, real income, inflation and exchange rate with an
elasticity of income slightly lower than unity and also an elasticity of exchange rate
significantly low.
Similarly, Mutluer and Barlas (2002) analyse broad money demand in Turkey
between 1987 and 2001, a period characterized by a process of financial sector
liberalization, implemented using various structural reforms and deregulations. Their
results indicate the existence of a long run relationship for real broad money in
Turkey, with a unitary income elasticity estimated. Also, the results show that, both
exchange rate and inflation rate have substantial impact on the Turkish broad money
demand.
Furthermore, Akıncı (2003) investigates the relationship between real money
balances, real income, and the opportunity cost variables in Turkey using quarterly
data for the period 1987Q1-2003Q3. The estimated results indicate that there exists a
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long run relationship between real currency issued, private consumption expenditure
as scale variable, interest rates on government securities and the exchange rate.
The empirical relationship between money, real income, interest rates and
expected exchange rate, is modelled by Civcir (2003) who also examines the
constancy of this relationship, especially in the light of financial reform, deregulation
of financial markets and financial crises in Turkey. The results obtained indicate the
existence of a stable real broad money demand relationship with a positive unitary
income elasticity confirming the quantity theory of money and negative opportunity
cost variables. The results also reveal that the demand for money in Turkey is stable,
despite the economic reforms and financial crises. Altınkemer (2004) investigates the
existence of a stable long-run money base demand and gives some implications for
Turkey’s implicit inflation targeting policy.
Halicioglu and Ugur (2005) analyse the stability of the narrow money demand
function (M1) in Turkey for the period 1950 - 2002. They estimate and test for the
stability of Turkish M1 by cointegration procedure proposed by Pesaran et al. (2001)
alongside CUSUM and CUSUMSQ stability tests. They demonstrate that there is a
stable money demand function and it could be used as an intermediate target of
monetary policy in Turkey.
Saatcoglu and Korap (2005) try to examine the money demand and its
determinants for the period 1987:Q1-2004:Q2. They construct an empirical money
demand model for Turkish economy, and compare the estimated results with the
findings of some other empirical money demand studies carried out on Turkish
economy.
Saatcoglu, el al (2006) investigate whether the money multiplier process in the
Turkish economy is stable and can be forecasted. Research results show that the
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processes which convert the base money supply into the final monetary aggregates are
unstable and decrease the effectiveness of monetary policies pursued by the Central
Bank of the Republic of Turkey (CBRT).
Akcaolayan, and Dommez
(2008) attempt to test the stability of money
demand relations for four money aggregates in Turkey. They use the Johansen
multivariate cointegration analysis covering the period 1990:Μ1-2005:Μ12. The
findings indicate that there is a stationary long-run relationship between the different
monetary aggregates, real income, domestic interest rates, foreign interest rates, and
the real effective exchange rate.
Finally, Ozdemir and Saygili (2010) analyze the parameter constancy of the
long run money demand function in Turkey. They argue that the conventional money
demand function is augmented by using proxies for macroeconomic uncertainty. As a
part of this methodology, they constructed a wide variety of measures that are
believed to be related with our definition of uncertainty, which is interpreted as the
loss of investors’ confidence regarding the well functioning of the economy. The
variables that are calculated for this purpose are the budget deficit to GDP ratio, the
variability of inflation, real output, basket and the Istanbul Stock Market Index at
different frequencies. Their results suggest that money balances, income and interest
spread are not cointegrated when the VAR system is missing a measure of economic
uncertainty. Thus, they find stable long run relations and coefficients when the correct
measures of uncertainty are introduced to the system.
4.
Data Analysis
It is necessary that all the empirical models should start by considering the
data generating process for the time series. This process should be able to generate all
the statistical properties of the series conditional on past information.
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In this analysis the monetary aggregate the real M1, (currency in circulation
plus demand deposits), is taken as a proxy for the relevant measures of money. We
use M1 as a proxy for the demand for money because the central bank is able to
control this aggregate more accurately than broader aggregates such as M2 and M3.
The choice of M1 is also relevant for Turkey because the central bank has been
targeting base money rather than broad monetary aggregates such as M2 and M3.
We also use the consumer price index, industrial production, and nominal
interest rate. Logarithm values were used for money supply, consumer price index,
industrial production, and nominal interest rate. All the data we use are from IMF
(International Monetary Fund) over the period 1989:1–2010:5. In this section we
analyse data-series by looking at the relevant figures of correlation and covariance.
(a) Figures:
Figures of the series give visual information about the data generating process. In
Figure.1 it can be inferred that M1, consumer price index, and industrial production
have upward trends; nominal interest rate are above their means for the period
1994Μ2 to 1994Μ5, and 2000Μ12 to 2001Μ2. From 2001:3 onwards, nominal
interest rates have a downward trend.
Figure 1
Looking at Figure 1, there is a very sharp decrease in output from 2008:7 to
2009:2. Nominal interest rate is at its maximum in 1994:3, but also in 2001:2.
Consumer price index is at its minimum from 1989:1 until 1991:3, but maximum in
2010:4. In the case of consumer price index there is a steady increase from 1989:1
onwards. From the figures it can be seen that all the variables included in the model
are affected by the 1994 and 2001 economic crises in Turkey. Accordingly, it
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becomes inevitable to test the stability of the money demand equations in this
analysis.
Figures of the levels of the real M1 variables, consumer price index, industrial
production, and nominal interest rate, appear to have non-constant mean, and give
some information about the non-stationarity. Instead, the figures of first differences
show no evidence of changing means.
(b) Correlation and Covariance:
The correlation and covariance matrices clarify the direction and the degree of
the relationships between variables in the system. Table.1 and Table.2 show the
correlation and covariance matrices for the system variables.
Table.1a
Table.1b
The correlation coefficients for the level of the variables are very high, but not
quite high for the first differences. Real M1 is highly and positively correlated with
consumer price index and industrial production. This implies that there is a short-run
homogeneity between M1 money and consumer price index and industrial production.
Thus an increase in the rate of growth of M1 money is expected to boost consumer
price index and industrial production.
Real M1 has a negative correlation with
nominal interest rate.
Covariance matrix, providing the information about the direction of the
relationship between variables, shows that almost all of the covariations are overhead.
This means that covariations between variables are related.
Table.2a
Table.2b
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Table.2a and Table.2b reveal that all of the covariations are overhead. On this
basis, it may be inferred that the variables in the model are highly related to each
other. For example, the covariation between a change in the real M1 money and the
rate of consumer price index is 48.47. This implies that a high inflation increases real
cash balances. Figure 2 presents the consumer price index and nominal interest rate.
Figure 2
As shown in Figure 2, before 2002, nominal interest is always above the
consumer price index, but after 2002 it is the consumer price index above the nominal
interest. Thus changes in the nominal interest could sustain an equal hedge against
consumer price index during the whole sample period.
5.
Model and Econometric Methodology
Modelling the real money demand in Turkey, requires careful consideration
not only for the choice of the relevant variables but also for the formulation of the
model. Price homogeneity is imposed to define the demand for money in real terms.
We assume that there is no money illusion. Hence, the real money demand is defined
to be an increasing function of industrial production and a decreasing function of
opportunity costs, i.e. nominal interest rate and inflation. The nominal interest rate
and consumer price index are considered to be the opportunity cost for money.
In Turkey, consumer price index is above the nominal interest rate after the
year 2002 (Figure 2). A high consumer price index reduces the real cash balances and
the relative attractiveness of financial assets. Thus, economic agents move towards
the speculative motive and move away from cash that buys real assets or moving to
dollarization in order to find good hedge against inflation. This is the main reason
why inflation rate is included in the model.
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Following Shigeyuki Hamori, Naoko Hamori (2008), the model includes
money supply, price index, output and nominal interest rate, which can be written as:
Mt
 L(Yt , Rt )
Pt
LY  0
LR  0
(1)
where
Mt represents nominal money supply for period t;
Pt represents the price index for period t; (the consumer price index (CPI)
Yt represents industrial production for period t; and
Rt represents the nominal interest rate for period t.
Increases in industrial production bring increases in money demand ( LY  0 ) and
increases in interest rates bring decreases in money demand ( LR  0 ).
Getting the log of Equation (1) we get the following function:
ln( M t )  ln( Pt )   0  1 ln( Yt )   2 ln( Rt )  ut
1  0
2  0
(2)
The problem confronting the estimation of the function for money demand is
that money demand, industrial production, price index, and interest rate can all be
characterized as non-stationary I(1) variables. As such, each variable can be explained
without any tendency to return to a long-run level. However, the theory expressed in
the equation asserts that there exists a linear combination of these non-stationary
variables that is stationary.
Unit root test
The Augmented Dickey–Fuller (ADF) (1979,1981) and Phillips-Perron (1988)
tests were used to determine the presence of unit roots in the data sets. The ADF test
is based on the estimate of the following regression:
k
X t   0  1t   2 X t 1   i X t  i  ui
(3)
i 1
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where, Δ is the first-difference operator, Xt is the natural logarithm of the series, δ0,
δ1, δ2, and αi are being estimated and ut is the error term. The null and the alternative
hypothesis for the existence of unit root in variable Xt is: H0:δ2=0 against Hε:δ2<0.
The PP unit root test is utilized in this case in preference to ADF unit root tests for the
following reasons. The PP tests do not require an assumption of homoscedasticity of
the error term (Phillips, 1987) and the test corrects the serial correlation and
autoregressive heteroscedasticity of the error terms.
Vector autoregressive estimation
A vector autoregressive model (VAR) describes the evolution of a set
of k variables (called endogenous variables) over the same sample period (t =1,..., T)
as a linear function of their p lags and possibly of their additional exogenous
variables.
A VAR allows the data to determine the precise model specification and treats
all variables as endogenous. Thus, a general polynomial distributed lag framework or
VAR (p) model can be written as follows:
yt = c + A1yt-1 + A2yt-2 +…+Apyt-p + et
(4)
where
yt is a k × 1 vector of endogenous variables.
c is a k × 1 vector of constants.
Ai is a k × k matrix (for every i = 1, ..., p) and
et is a k × 1 vector of error terms
For the vector of error terms we get:
E (et )  0 (every error term has mean zero).
E (et et)  
(the covariance matrix of error terms is Ω (a k × k positive
definite matrix).
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E (et etk )  0 (for any non-zero k, there is no correlation across time. In other words
there is no serial correlation in individual error terms).
Co-integration tests
The presence of long run equilibrium relationship between dependent and
independent variables is referred to as cointegration. The two common tests for
cointegration are the procedure of Engle and Granger (1987) and the procedure of
Johansen and Juselius (Johansen and Juselius, 1990, Johansen, 1992).
Following Johansen procedure, let us consider a VAR(p) model adapted to the
VEC representation:
p 1
Yt  Yt 1   i Yt i  X t  et
(5)
i 1
Granger’s representation theorem asserts that if the coefficient matrix Π has reduced
Rank r < k then there exist k  r matrices  and  each with rank r such that
    and  Yt is I(0), r is the number of cointegrating relations (the cointegrating
rank) which we identify using the trace statistics. Moreover, each column of  is a
cointegrating vector. The elements of  represent the adjustment parameters in the
VECM. i capture the short-run effects of the time series. Johansen’s method consists
of estimating the matrix Π from an unrestricted VAR with a maximum likelihood
technique and testing whether we can reject the restrictions implied by the reduced
rank of Π.
Order of integration of the variables
We should note that all variables used, have to be of the same order of
integration. So we have the following cases:
1)
All the variables are I(0) (stationary): in this case in a VAR model the
variables are in their levels
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2)
All the variables are integrated I(d) (non-stationary) with d>0:
2a)
The variables are cointegrated: the error correction term has to be included in
the VAR. The model becomes a Vector Error Correction Model (VECM) which can
be characterised as a restricted VAR model. Thus, the VAR (p) model can be written
as: (Engle and Granger 1987).
Δyt = c + Π1Δyt-1 + Π2Δyt-2 +…+ΠpΔyt-p + biECt-1 + et (6)
where
ECt-1 is the error correction term.
bi is a k × 1 vector.
2b)
The variables are not cointegrated: the variables have first to be differenced d
times and one has a VAR in difference. Thus, the VAR (p) model can be written as
follows:
Δyt = c + Γ1Δyt-1 + Γ2Δyt-2 +…+ΓpΔyt-p + et (7)
Vector error correction models
The variables are associated with the VAR approach at the cointegration level,
before we can form the VECM. So, we need to ensure that the variables are
cointegrated. There are other considerations where more than one cointegrating vector
exists, thus we can theoretically have more than one error correction term.
The dynamic relationship includes the lagged value of the residual from the
cointegrating regression, besides the first differences of variables that appear in the
long-run relationship. The inclusion of the variables from the long-run relationship
can capture short run dynamics. It is essential to test whether the long-term
relationship established in the model gives the short-run disturbances. Thus, a
dynamic error correction model, which forecasts the short-run behavior, is estimated
on the basis of cointegration relationship. For this reason the residual with one-time
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lag derived from the cointegration vector, is being incorporated into the general error
correction model (ECM) (Jayasooriya 2010).
Granger Causality tests
Engle and Granger (1987) determine the duality between cointegration and the
vector error correction models (VECM). Furthermore, they show that the application
of causality should be done by the VECM since it is the ideal instrument for causality
examination because among other things, it determines the speed of convergence of
the relevant variables to their equilibrium.
Since we found evidence of cointegration, there must be either unidirectional
or bidirectional Granger causality, because at least one of the error correction terms
should be significantly different from zero by the definition of cointegration. The
VECM approach, apart from showing the direction of Granger-causality among the
variables, enables to distinguish between ‘short-run’ and ‘long-run’ Granger causality.
Testing Stability of the Demand for Money
Testing for stability of money demand is important as money supply is one of
the key instruments of monetary policy. If money demand is stable then money supply
is the most suitable monetary policy instrument but if money demand function is not
stable, central bank should use interest rate as the most appropriate instrument for the
conduct of monetary policy. It is argued that evolution and development of the
financial market together with innovation in information technology brings in element
of sensitivity in the demand for broad money in the economy. (Singh and Pandey
2009). For estimating the money demand function, we have used the conventional
methods for the test of stability of the money demand function, these tests include
CUSUM, CUSUMSQ and recursive estimation technique.
Impulse response function
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The impulse response functions can be used to produce the time path of the
dependent variables in the VAR, to make shocks from the explanatory variables. The
impulse response function defines the effect that a random impulse shock has upon
the endogenous variables of VAR model. Usually, these shocks are expressed using
the standard deviations of the disturbance terms (one or two standard deviations).
Thus, the impulse response function describes the implications on the endogenous
variables in a VAR model for a number of future periods when disturbance terms are
volatile. VAR models are considered suitable for the achievement of satisfying
predictions due to their structure and also to the capability of the estimation of
impulse response function and the variance decomposition.
6.
Empirical Results
The current paper investigates a long term dynamic relationship between
demand for money and consumer price index, industrial production, and the nominal
interest rate in the case of Turkey from 1989:M1 to 2010:M5 by using a dynamic
model. The empirical model specification applies a unit-root test, cointegration,
vector error correction model and the impulse response function. The results of the
dynamic model are presented in the following section
Unit root test
Based on the unit root test as depicted in table 3, all unit root tests yield
remarkably similar results for all variables namely LMP1, LY and LR, which are non
stationary in their levels I(0) but become stationary in their first differences I(1).
Thus, we conclude that all series are I(1) at the 1% level of significance.
Table.3
Johansen Cointegration Test
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Johansen and Juselius’s (1990) cointegration method was used for the
cointegration analysis. The order of lag-length was determined by the Schwarz
Information Criterion (SIC) and the Akaike Information Criterion (AIC). Johansen
and Juselius, procedure test results are presented in table 4.
Table.4
The test statistics reject the null hypothesis of no cointegrating relation at the 5
% significance level, hence there is a cointegration vector (see the trace test and the
maximal-eigenvalue statistics for cointegration test in table 4). This indicates that
there is a long run relationship between money demand, industrial production,
consumer price index, and nominal interest rate over the sample period under
investigation.
The next step is to report the Granger causality test results obtained by the
vector auto regression (VAR). Since a cointegration vector exists, we run the Granger
test with error correction terms from the cointegrating equations included in a
regression with the variables in their first differences (ΔLMP1, ΔLY and ΔLR). (See
equation 6 for the error correction model.) Results are reported in table 5.
Table.5
Results from table 5 indicate a short-term causality relationship between
industrial production and demand for money, directed from the former to the latter.
Also we found a causal relationship from the nominal interest rate towards industrial
production and finally a short-term causal relationship between money demand and
nominal interest rate directed from money demand to interest rate. The same causal
relationships have also been depicted in the long run.
Testing Stability of the Demand for Money
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Figures 3 and 4 present the plot of CUSUM and Cumulative sum of squares at
the 5% level of significance. The plots of the CUSUM and CUSUMQ show instability
of the demand for money function during the period 1989:M1-2010:M5.
Figure 3
Figure 4
The results of the CUSUM and CUSUMQ of Square are no surprising for the
case of the Turkish economy. Τhis instability in the demand for money may be due to
the fact that during this time Turkish economy has undergone political uncertainties.
(Turkish economy has been through two crises and various reformations).
The plot of recursive residuals clearly shows any presence of instability as it
reaches the upper band at around 2006 and the lower band after 1991 (see figure 5).
Other than these periods demand for money has been stable in Turkey.
Figure 5
Impulse response function
We begin our empirical investigation by testing for the order of the VAR
using several information criteria. Based on the overall results of these tests reported
in table 4, the optimal lag length was determined to be 2. The VARs are estimated
using monthly data from January 1989 through Μay 2010. The estimated VAR
coefficients are not very interesting themselves and thus omitted. Instead, we focus on
the impulse responses and the variance decompositions.
Figure 6 plots the impulse responses of LMP1, LY, LR over a horizon of 36
months. Standard errors are calculated by the Monte Carlo method, with 1,000
repetitions (of ± 2 standard deviations).
Figure 6
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Impulse responses suggest that shocks in LMP1 have a negative impact on the
variable itself, there is also a small positive trend in the case of LY for the whole period
whereas we observe a drop of LR for the fist 15 months followed by a stabilisation.
Shocks in LY cause a slight fall in LMP1 for the first month followed by a
small constant increase. Also, for LY itself we observe a continuous declining trend
for the whole period (apart from the 2nd and 3rd months). LP decreases in the first 15
months followed by stabilisation.
Shocks in LR cause a slight decrease on LMP1 in the first month followed by
stabilisation. LY is stable throughout the whole period under investigation. Finally,
LR shows a decreasing trend for the whole period (apart from the 2nd and 3rd months).
The results from variance decompositions are reported in figure 7, and table
6a, table 6b and table 6c.
Figure 7
From Figure 7 we observe that the highest percentage error variance of
demand for money (LMP1) is due to interest rate (LR) by 60%, by 40% from the
demand for money (LMP1), and from the industrial production (LY) by 2%.
Similarly, the highest percentage error variance of industrial production (LY) is
originating from the industrial production (LY) by 40%, from the interest rate (LR)
by 38% and by 5% from the demand for money (LMP1). Finally, the highest
percentage error variance of interest rate (LR) is originating from the interest rate
(LR) by 90%, from the demand for money (LMP1) by 10% and by 2% from the
industrial production (LY).
The forecast error variance decomposition indicates the proportion of the
movements in a sequence due to its “own” shocks versus shocks to the other
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variables. The overall results of this exercise are reported in table 6a, table 6b and
table 6c.
Table.6a
The results from variance decompositions suggest that, over a 36-month
horizon, 21.78 percent of the forecast error variance of industrial production (LY) can
be accounted by shocks of money demand (LMP1).
Table.6b
The results from variance decompositions suggest that, over a 36-month
horizon, 75.54 percent of the forecast error variance of interest rate (LR) can be
accounted by shocks to money demand (LMP1).
Table.6c
Table 6c indicates that demand for money (LMP1) explains nearly 46 percent
of its forecast error variance.
7. Conclusions
A basic principle of monetary theory is that the monetary authority can control
the monetary aggregates and forecast their growth paths. Under these conditions,
monetary policy can check whether demand for money is stable and whether there
exists any long-run relationship between demand for money and its determinants.
In this article, we attempt to examine the stability of money demand function
in the case of Turkey for 1989:M1-2010:M5. The period under investigation is
characterized by high inflation, financial liberalization, capital account liberalization,
and financial innovation driven mainly by an increasing government debt (see Irfan
Civcir 2003). Besides the stability of money demand, we investigate the potential
long-run relationship between demand for money and its determinants. Using
cointegration Johansen techniques, we are able to demonstrate that there is a long-run
21
relationship between the demand money aggregate and its determinants: industrial
production, consumer price index and nominal interest rate.
Results from the error-correction model revealed a short-run and a long-run
causal relationship between industrial production and demand for money directed
from the former to the latter. Similarly, with nominal interest rates and industrial
production with the short and long-run relationship been directed from the nominal
interest rate towards industrial production. Finally, a short and long-run relationship
was detected between demand for money and nominal interest rate directed from the
former to the latter.
We have also tried to incorporate the short and long-run dynamics of money
demand function in order to perform a more robust account of the stability of money
demand function. To this end, we utilized the CUSUM and CUSUMSQ stability tests.
The results show that the process that extends the basic money supply to the final
monetary aggregates is unstable, decreasing the effectiveness of monetary policies
implemented by the Central Bank of the Republic of Turkey (CBRT).
Finally, with the impulse response function estimation, using a recent
alternative approach by Pesaran and Sin 1998 (known as the generalised impulse
response analysis based on variance decompositions), we predicted our model
variables. The findings from the impulse response function estimation are consistent
with the view that shocks in interest rates cause the greatest variance in the demand
for money (60%) followed by industrial production (38%).
The latest financial crises of November 2000 and February 2001 showed that
it is very important to implement serious monetary programmes because money
demand regulations alongside interest policies play a very important role in the
Turkish financial system. Hence, they should be carefully regulated in order to retain
22
stable and low levels of inflation. Thus, monetary policy gained importance after the
aforementioned crises and the governments tried to use the policies according to the
Monetarist’s rules (i.e. rule-based policies).
The findings of the current study suggest that demand for money can be used
as a target of monetary policy in Turkey. As far as the Turkish central bank’s
monetary policy is concerned, we propose that stability of a money demand function
will reduce the uncertainty associated with the financial system and will increase the
credibility for a stable and developmental environment.
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Figure 1: Series in levels and first differences
M1
P
120000
160
100000
120
80000
60000
80
40000
40
20000
0
0
90
92
94
96
98
00
02
04
06
08
90
92
94
96
98
Y
00
02
04
06
08
02
04
06
08
R
130
500
120
400
110
100
300
90
80
200
70
60
100
50
0
40
90
92
94
96
98
00
02
04
06
08
90
92
94
96
98
00
26
DM1
DP
28000
4
24000
3
20000
16000
2
12000
8000
1
4000
0
0
-4000
-1
-8000
90
92
94
96
98
00
02
04
06
08
90
92
94
96
DY
98
00
02
04
06
08
02
04
06
08
DR
20
400
300
10
200
100
0
0
-10
-100
-200
-20
-300
-30
-400
90
92
94
96
98
00
02
04
06
08
90
92
94
96
98
00
Figure 2: Nominal interest and consumer price index
500
400
300
200
100
0
90
92
94
96
98
P
00
02
04
06
08
R
27
Table.1a: Correlation Matrix (Levels)
Μ1
P
Y
Μ1
P
Y
R
0.929885
0.840009
-0.529041
0.894402
-0.566693
R
-0.507635
Table.1b: Correlation Matrix (First Differences)
ΔΜ1
ΔP
ΔY
ΔΜ1
ΔP
ΔY
ΔR
-0.027420
0.194547
0.031309
0.029538
-0.076714
ΔR
-0.006545
Table.2a: Covariance Matrix (Levels)
Μ1
P
Y
Μ1
P
Y
R
1528578.2
532883.02
-742844.51
929.031
-1302.88
R
-450.578
Table.2b: Covariance Matrix (First Differences)
ΔΜ1
ΔP
ΔY
ΔΜ1
ΔP
ΔY
ΔR
LMP1
LY
LR
48.4730
2920.93
2662.52
0.15380
-2.26250
ΔR
-1.63956
Table.3: ADF and PP unit root tests
ADF
Level
Difference
Level
-0.962 (1)
-20.67 (0)***
-1.197 [4]
-0.914 (13)
-4.215 (12)***
-2.394 [23]
-1.098 (1)
-24.85 (0)***
-1.528 [9]
PP
Difference
-20.61 [3]***
-72.06 [79]***
-30.53 [13]***
Notes:
1. *** denote significant at 1% level of significance.
2. The numbers within parentheses followed by ADF statistics represents the lag length of the dependent variable
used to obtain white noise residuals.
3. The lag lengths for ADF equation were selected using Akaike Information Criterion (AIC).
4. Mackinnon (1991) critical value for rejection of hypothesis of unit root applied.
5. The numbers within brackets followed by PP statistics represent the bandwidth selected based on Newey West
(1994) method using Bartlett Kernel.
6. LMP1=ln(M1)-ln(P).
7. Exogenous:Constant.
28
Table.4: Johansen and Juselius’s Cointegration Test Results
Null
5% critical value
Hypothesis
Trace test
Max-Eigen
Trace test
Max-Eigen
LMP1, LY, LR (Order VAR = 2)
r=0
45.31
33.79
29.79
21.13
11.52
11.41
15.49
14.26
r≤1
r≤2
0.108
0.108
3.84
3.84
Notes:
1. Critical values derive from Osterwald – Lenum (1992).
2. r denotes the number of cointegrated vectors
3. Akaike and Schwarz criterion are used for the order of VAR model
Table.5: Multivariate Granger causality results
F-test
ΔLY
2.14*
ΔLMP1
ΔLMP1
ΔLY
ΔLR
1.17
2.32*
T-test
ECM
-4.68332***
-2.36179***
-2.12932**
ΔLR
1.47
1.87*
0.23
Note: ***, ** and * indicate significance at the 1%, 5% and 10% levels of significance, respectively.
Δ is the first different.
Figure 3. Cumulative Sums of Recursive Residuals
60
40
20
0
-20
-40
-60
90
92
94
96
98
CUSUM
00
02
04
06
08
5% Significance
29
Figure 4. Cumulative Sums of Squares of Recursive Residuals
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
90
92
94
96
98
00
02
04
06
08
06
08
CUSUM of Squares
5% Significance
Figure 5. Recursive Residual Plots
.6
.4
.2
.0
-.2
-.4
-.6
90
92
94
96
98
00
02
Recursive Residuals
04
± 2 S.E.
30
Figure 6: Response to Generalized One S.D. Innovations ± 2 S.E.
Response to Generalized One S.D. Innovations ± 2 S.E.
Response of LMP1 to LMP1
Response of LMP1 to LY
Response of LMP1 to LR
.10
.10
.10
.05
.05
.05
.00
.00
.00
-.05
-.05
-.05
-.10
-.10
-.10
-.15
-.15
5
10
15
20
25
30
35
-.15
5
Response of LY to LMP1
10
15
20
25
30
35
5
Response of LY to LY
.08
.08
.04
.04
.04
.00
.00
.00
-.04
-.04
-.04
10
15
20
25
30
35
5
Response of LR to LMP1
10
15
20
25
30
35
5
Response of LR to LY
.3
.3
.2
.2
.2
.1
.1
.1
.0
.0
.0
-.1
-.1
-.1
-.2
5
10
15
20
25
30
35
20
25
30
35
10
15
20
25
30
35
30
35
Response of LR to LR
.3
-.2
15
Response of LY to LR
.08
5
10
-.2
5
10
15
20
25
30
35
5
10
15
20
25
31
Figure 7: Variance Decomposition
Variance Decomposition ± 2 S.E.
Percent LMP1 variance due to LMP1
Percent LMP1 variance due to LY
Percent LMP1 variance due to LR
120
120
120
100
100
100
80
80
80
60
60
60
40
40
40
20
20
20
0
0
0
-20
-20
-20
5
10
15
20
25
30
35
5
Percent LY variance due to LMP1
10
15
20
25
30
35
5
Percent LY variance due to LY
120
120
100
100
100
80
80
80
60
60
60
40
40
40
20
20
20
0
0
0
-20
-20
-20
10
15
20
25
30
35
5
Percent LR variance due to LMP1
10
15
20
25
30
35
5
Percent LR variance due to LY
120
120
100
100
100
80
80
80
60
60
60
40
40
40
20
20
20
0
0
0
-20
-20
-20
10
15
20
25
30
35
5
10
15
20
25
30
20
25
30
35
10
15
20
25
30
35
Percent LR variance due to LR
120
5
15
Percent LY variance due to LR
120
5
10
35
5
10
15
20
25
30
35
Table.6a: Generalized Forecast Error Variance Decomposition of LY
Variance Decomposition of LY
Months
LMP1
LY
LR
S.E
6
0.430
94.204
5.364
0.1205
(1.016)
(3.476)
(3.353)
12
1.515
81.793
16.690
0.1489
(2.282)
(8.437)
(8.212)
24
4.824
59.957
35.217
0.1879
(5.233)
(13.874)
(12.864)
36
7.625
46.975
45.399
0.2178
(7.376)
(15.786)
(14.221)
32
Table.6b: Generalized Forecast Error Variance Decomposition of LR
Variance Decomposition of LR
Months
LMP1
LY
LR
S.E
6
6.228
0.683
93.088
0.4227
(3.878)
(1.663)
(3.989)
12
8.588
1.293
90.118
0.5302
(5.672)
(3.267)
(5.715)
24
11.611
2.200
86.188
0.6619
(8.472)
(6.027)
(8.543)
36
13.301
2.726
83.972
0.7554
(10.113)
(7.731)
(10.292)
Table.6c: Generalized Forecast Error Variance Decomposition of LMP1
Variance Decomposition of LMP1
Months
LMP1
LY
LR
S.E
6
83.479
0.449
16.071
0.1743
(5.317)
(1.117)
(5.286)
12
63.738
0.828
35.433
0.2505
(9.992)
(2.540)
(9.797)
24
44.188
1.668
54.143
0.3687
(13.081)
(5.199)
(12.547)
36
36.350
2.324
61.324
0.4552
(13.973)
(7.157)
(13.329)
33