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MONEY DEMAND IN ROMANIAN ECONOMY, USING MULTIPLE REGRESSION METHOD AND UNRESTRICTED VAR MODEL

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The paper describes the money demand in Romanian economy using two econometrics models. The first model consist in a multiple regression between demand money, monthly inflation rate, Industrial production Index and the foreign exchange rate RON/Euro. The second model (Unrestricted Vector AutoRegressive model) is applied for the same variables used in the first model. Identifying a statistically strong model, capable of stable estimations for the money demand function in Romania’s economy constitutes a prerequisite to the application of an efficient monetary policy.
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Quantitative Methods Inquires

MONEY DEMAND IN ROMANIAN ECONOMY, USING
MULTIPLE REGRESSION METHOD AND
UNRESTRICTED VAR MODEL

Mariana KAZNOVSKY

PhD Candidate, University of Economics, Bucharest, Romania
Monetary and Financial Statistics Division, National Bank of Romania, Bucharest, Romania

E-mail:
Mariana.Kaznovsky@bnro.ro


Abstract:
The paper describes the money demand in Romanian economy using two
econometrics models. The first model consist in a multiple regression between demand
money, monthly inflation rate, Industrial production Index and the foreign exchange rate
RON/Euro. The second model (Unrestricted Vector AutoRegressive model) is applied for the
same variables used in the first model. Identifying a statistically strong model, capable of
stable estimations for the money demand function in Romania’s economy constitutes a
prerequisite to the application of an efficient monetary policy.

Key words:
money demand; unrestricted VAR model; Romania

Multiple regression estimation of Romanian money demand function


The theory underlying money demand function is based on the classical
macroeconomic model of Hicks & Hansen IS-LM, specifically LM curve. The theoretical
hypothesis (assumptions) of the dual equilibrium model for the money market in an open
economy are: the perfect mobility of capital, uncovered interest rate parity principle,
monetary policy conducted by the central bank are using the short term interest rate
variable as the operational one without affecting the stability of the exchange rate of the
national currency.
The LM curve is defined by the all possible combinations of interest rate and income
levels for which the demand of money is equal with money supply (Figure 1.).

Figure 1. LM curve

187


Quantitative Methods Inquires
The money demand function is a synthetic way of measuring the dependence
between, on the one side, the monetary aggregates - as the money issued by the monetary
financial institutions: credit institutions and money market funds, and used as financial
resource by the non-banking entities: non-issuing money institutions, and, on the other side,
the money consumers in the economy.
The classic model [3;4]1 estimates this correlation by the degree of explanation of
the endogenous variable “monetary aggregate” by the following exogenous variables:
monthly price growth rate, value of the economic output (GDP, industrial production value),
average passive interest rate practiced by the credit institutions as an expression of the “price
of money” and other variables expressing the cost of opportunity for possessing the currency
- like exchange rate, the dynamics of the domestic capital index or a foreign capital index
related to the analyzed economy. Taking into account changes in the international oil
markets as an indicator of foreign restrictions could be useful in explaining the money
demand pattern.
The specific choice of variables used to estimate the demand of money depends on
the working hypotheses, on the availability of data with adequate frequency, as well as on
conclusions of previous studies and research works regarding the significance of correlations
that point to one indicator being more reliable than others in approximating the variable.
In order to express the monetary aggregate in the Romanian economy, the choice
has been made for the broad money indicator M2 (known as broad money up to 2007, after
which M3 was introduced, M2 becoming the intermediary monetary aggregate). The
explanation of the use of M2 resides in the higher degree of coverness of the financial
instruments by this indicator. Narrow money M1 is almost designed to be a proxy measure of
the exchange transaction incentives of money only, while broad money M2 is designed to
quantify also the accumulation of value purpose of holding money.
Although, the exogenous variables have to be restricted to the most significant
ones, thus avoiding multicollinearity. Out of purely practical reasons, the industrial
production index has been selected to measure the economic output, whereas for the cost of
money we considered significant the average interest rate for one month as a liability of
monetary financial institutions. For medium and long term maturity we used the interest rate
of the one year government bonds. Longer maturities have been left out because of the
discontinuity in issuance, in relation to the investor lack of preference for medium and long
term maturities.
As an indicator of price increases, we used the monthly Consumer Price growth
rate, as the GDP deflator is available, at best, quarterly, starting with 1998.
Our study has been compensating for the inflationary component by studying the
dependence between the deflated monetary aggregate and the real money demand factors.
For the following regressors the ‘t’ statistic significance of the coefficients of the
money demand function has been confirmed: industrial production index, real money
balances as the log level recorded three months ago, monthly inflation rate and the foreign
exchange rate (leu/Euro). The money demand elasticity in respect of interest rate (as the
average cost of monetary financial institutions for the borrowed resources and, implicitely, as
the rate of return of deposits made by non-banking entities with the banks) was not being
confirmed at the 10% level of significance. Thus, the conclusions of some previous work
papers that the interest rate channel is not efficiently working in the romanian economy are
confirmed by the statistical data [1]. The weak sensitivity of the real variables block could be

188


Quantitative Methods Inquires
explained by the rigidity of the economy to the monetary impulses due to the specific
structural changes in our emerging economy. On the other side, the National Bank of
Romania’monetary policy was focused on the monetary aggregates (base money) as the
operational target, the exogenity of interest rate being a practical issue in the nominal
variables block.
Thus, we have estimated the equation of the money demand for the Romanian
economy using the following specific version:

m = a + a m
+ a prodind
?a r inf l + a curs _ eur + ?
t
0
1
t ?1
2
t ?3
3
t
4
t
t
where:
m is the real monetary aggregate, deflated using the CPI (real M2) and seasonally adjusted,
decimal logarithm values being considered; seasonally adjustment has been performed
using the Census X11 method, considering the multiplicative method. The need to isolate
and detach the seasonal component from the series of the monetary aggregate was imposed
by the known peak effect during summer and holidays. Introducing unadjusted series would
have led to rejection of the coefficients, the statistical significance being infirmed with a
probability of 90%.
rinfl is the monthly CPI rate, logarithm values;
prodind is the Industrial Production Index, logarithm vales
curs_eur is the foreign exchange rate RON/Euro, logarithm values.

Table 1 presents the estimators of the regression coefficients, obtained using Eviews
4 software, for the time horizon for January 1992-December 2005, statistical series having
monthly frequency.

Table 1. Estimators of the regression coefficients
Dependent Variable: LOG(M2_SA/p)
Method: Least Squares
Date: 02/27/08 Time: 15:22
Sample(adjusted): 1992:04 2005:12
Included observations: 165 after adjusting endpoints
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
0.192377
0.031722
6.064535
0.0000
LOG(m2_SA(-1)/p(-1))
0.951809
0.008594
110.7478
0.0000
LOG(rinfl)
-0.012963
0.002819
-4.599288
0.0000
LOG(PROD_IND(-3))
0.054574
0.027316
1.997866
0.0474
LOG(CURS_EUR)
0.048924
0.011085
4.413621
0.0000
R-squared
0.999853
Mean dependent var
4.075405
Adjusted R-squared
0.999850
S.D. dependent var
1.911885
S.E. of regression
0.023439
Akaike info criterion
-4.639032
Sum squared resid
0.087899
Schwarz criterion
-4.544912
Log likelihood
387.7201
F-statistic
272760.0
Durbin-Watson stat
1.789483
Prob(F-statistic)
0.000000

A powerful influence of the autoregressive component upon the deflated broad
money has been detected (the estimated coefficient being 0.95); the value of the industrial
production as an approximate value of the real aggregate supply, is positively correlated
with monetary aggregate, but transmission of this influence is produced with a time lag of 3
months. Thus, the changes in real variables is reflected in values of the nominal variables

189


Quantitative Methods Inquires
after 3 months, but the influence is not strong (estimated elasticity is 0.05: at a change in the
industrial production index with 1%, the reaction of the broad money over 3 months is of
size 0.05%). Estimation of simultaneous correlation of this link has been infirmed by the “t”
test, at a probability level of 90%.
The opportunity cost of holding the money has been approximated by means of
introducing the leu/Euro exchange rate: the equation confirms the positive correlation
between the exchange rate and the real broad money. The national currency depreciation
influences the growth of money demand in real terms, as a consequence of the considerable
weight of the foreign currency denominated part of the monetary aggregate. The shifting
from local currencies to USD/Euro, as a process of substitution of the national currency, is
characteristic for emerging markets, marked by significant changes in economic structure,
and for which the tax of holding the money (inflation rate) and inflationary expectations are
high.
The inverse correlation between the inflation rate and the real broad money is
statistically confirmed by the “t” test. Interesting to observe is the small influence of prices
upon the real broad money.
Stationarity of the data has been verified with the ADF (Augumented Dicky-Fuller)
test, for the case of a liniar trend, a constant and eight lags, corresponding to the timespan
of january 1992-december 2005 (results are presented in table 2).
The degree of explanation brought by the exogenous variables in their entirety,
contributes in proportion of 99% (adjusted R2 coefficient) to the obtaining of values for the
adjusted series of money demand, as is visible from figure 2.
The errors terms resulted from the regression, represented as a blue line has been
tested for autocorrelation: the Durbin-Watson statistic confirms the rejection of the
autocorrelation in the residual series; as a consequence, the regression parameters are
relevant and statistically significant.
8
6
4
0.15
2
0.10
0.05
0
0.00
-0.05
-0.10
93 94 95 96 97 98 99 00 01 02 03 04 05

Figure 2. Adjusted versus real money demand





190


Quantitative Methods Inquires
Tabel 2. Augumented Dicky-Fuller
Augmented Dickey-Fuller Test for real broad money M2/p
Sample(adjusted): 1992:06 2005:12
ADF Test Statistic
7.864602 1% Critical Value*
-3.4715


5% Critical Value
-2.8792


10% Critical Value
-2.5761
*MacKinnon critical values for rejection of hypothesis of a unit root.










Augmented Dickey-Fuller Test for industrial production index prod_ind
Sample(adjusted): 1992:06 2005:12
ADF Test Statistic
-7.769099 1% Critical Value*
-3.4715


5% Critical Value
-2.8792


10% Critical Value
-2.5761
*MacKinnon critical values for rejection of hypothesis of a unit root.










Augmented Dickey-Fuller Test for exchange rate leu/euro
Sample(adjusted): 1992:06 2005:12
ADF Test Statistic -3.205847 1% Critical Value* -3.4715


5% Critical Value
-2.8792


10% Critical Value
-2.5761
*MacKinnon critical values for rejection of hypothesis of a unit root.










Augmented Dickey-Fuller Test for inflation rate
Sample(adjusted): 1992:06 2005:12
ADF Test Statistic -3.205847 1% Critical Value* -3.4715


5% Critical Value
-2.8792


10% Critical Value
-2.5761
* MacKinnon critical values for rejection of hypothesis of a unit root.

Estimating the reaction of the broad money to shocks in real economy
variables (VAR model for the money demand in the economy)

An estimation of the correlation between the real exogenous variables and money
aggregates based on the UVAR (Unrestricted Vector AutoRegressive) with three lags has
been applied for the same variable used in the multiple regression. The system of
simultaneous equations comprises thus, the following variables: real broad money M2
(seasonally adjusted levels), IPI (Industrial production index), inflation rate, leu/Euro foreign
exchange rate. Series are monthly and covers the years 1992-2005; ADF stationarity tests
have shown the stationarity of the series of broad money, foreign exchange rate and
inflation rate with a probability of 95%.
The model specification is as follows:


191


Quantitative Methods Inquires
3
log(M _ SA(t) / p(t)) = a + ? a log(M SA(t ? i) / p(t ? i)) +

2
0
1i
2 _
i 1
=
3
3
3
+? a log( prod _ ind(t ? i)) + ? a log(curs(t ? i)) + ? a log(r inf l(t ? i)) + u (t)
2i
3i
4i
1
i 1
=
i 1
=
i 1
=
3
3
log( prod _ ind (t)) = b + ? b log(M SA(t ? i) / p(t ? i)) + ? b log( prod _ ind(t ? i)) +
0
1i
2 _
2i

i 1
=
i 1
=
3
3
+?b log(curs(t ? i)) + ?b log(r inf l(t ? i)) + u (t)
3i
4i
2
i 1
=
i 1
=
3
3
log(curs(t)) = c + ? c log(M SA(t ? i) / p(t ? i)) + ? c log( prod _ ind(t ? i)) +
0
1i
2 _
2i

i 1
=
i 1
=
3
3
+? c log(curs(t ? i)) + ? c log(r inf l(t ? i)) + u (t)
3i
4i
3
i 1
=
i 1
=
3
3
log(r inf l(t)) = d + ? d log(M SA(t ? i) / p(t ? i)) + ? d log( prod _ ind(t ? i)) +
0
1i
2 _
2i

i 1
=
i 1
=
3
3
+? d log(curs(t ? i)) + ? d log(r inf l(t ? i)) + u (t)
3i
4i
4
i 1
=
i 1
=
where i=1,3.
uj (j=1,4) are the regression residuals called innovations or shocks. The corresponding
innovation is, thus, that part of the evolution of the variable that neither be explained by its
past values (own history), nor by other variables of the model.
The VAR method concentrates mainly on studying the impact of every shock upon
every variable of the system of equations; this analysis is being performed by impulse
response functions, by factorial decomposition of variance.
Table 3. comprises estimated coefficient of the VAR model, obtained with Eviews 4
software.
The impulsion response functions graphically represent the evolution of these
shocks in time across 10 months, identifying the maximum impact upon variables taken into
account by the model; the sizing of these dependencies between innovations and model
variables is expressed in relative terms, that is, standard deviations of the shocks.

Tabel 3. The estimated coefficients of VAR model
Sample(adjusted): 1992:04 2005:12
Included observations: 165 after adjusting endpoints
Standard errors and t-statistic in brackets
LOG(M2_SA/p)
LOG(PROD_IND)
LOG(CURS_EUR)
LOG(RINFL)
LOG(M2_SA(-1)/p(-1))
1.107870
-0.151211
0.376171
-1.267625

(0.09407)
(0.24728)
(0.15506)
(2.45033)

(11.7771)
(-0.61150)
(2.42604)
(-0.51733)





LOG(M2_SA(-2)/p(-2))
0.107563
0.322834
-0.960039
-1.195572

(0.13958)
(0.36690)
(0.23006)
(3.63570)

(0.77064)
(0.87989)
(-4.17291)
(-0.32884)





LOG(M2_SA(-3)/p(-3))
-0.215949
-0.246611
0.616877
2.005835

(0.09335)
(0.24538)
(0.15387)
(2.43154)

(-2.31338)
(-1.00501)
(4.00918)
(0.82492)





LOG(PROD_IND(-1))
0.034269
-0.261478 -0.040848
1.036583

(0.02889)
(0.07593)
(0.04761)
(0.75244)


(1.18632)
(-3.44352) (-0.85791)
(1.37764)





LOG(PROD_IND(-2)) -0.017588
-0.315671 -0.105047
0.614014

(0.02858)
(0.07512)
(0.04710)
(0.74434)

192


Quantitative Methods Inquires

(-0.61547)
(-4.20240) (-2.23021)
(0.82491)





LOG(PROD_IND(-3))
0.047984
-0.313506
0.018482
0.014463

(0.02894)
(0.07608)
(0.04771)
(0.75393)

(1.65781)
(-4.12050)
(0.38740)
(0.01918)





LOG(CURS_EUR(-1))
-0.073281
-0.128218
1.496446
3.231345

(0.04646)
(0.12213)
(0.07658)
(1.21018)

(-1.57730)
(-1.04987)
(19.5411)
(2.67013)





LOG(CURS_EUR(-2))
0.133757
0.263857
-0.706407
-3.294675

(0.07548)
(0.19840)
(0.12441)
(1.96600)

(1.77219)
(1.32991)
(-5.67820)
(-1.67583)





LOG(CURS_EUR(-3))
-0.061791
-0.050815
0.157700
0.493682

(0.04544)
(0.11945)
(0.07490)
(1.18363)

(-1.35983)
(-0.42542)
(2.10550)
(0.41709)





LOG(RINFL(-1))
0.016839
-0.010827
0.011044
0.295220

(0.00376)
(0.00990)
(0.00621)
(0.09807)

(4.47250)
(-1.09394)
(1.77960)
(3.01028)





LOG(RINFL(-2))
-0.001959
-0.006298 -0.011551
0.148286

(0.00452)
(0.01189)
(0.00746)
(0.11784)

(-0.43299)
(-0.52963) (-1.54912)
(1.25840)





LOG(RINFL(-3))
-0.003161
-0.020569
0.011172
0.153754

(0.00389)
(0.01023)
(0.00641)
(0.10132)

(-0.81272)
(-2.01157)
(1.74250)
(1.51746)





C
0.073499
0.173660
-0.071412
0.423674

(0.03838)
(0.10088)
(0.06326)
(0.99964)

(1.91519)
(1.72144)
(-1.12892)
(0.42382)
R-squared
0.999864
0.237022
0.999321
0.668904
Adj. R-squared
0.999854
0.176787
0.999268
0.642765
Sum sq. resids
0.081358
0.562183
0.221043
55.20174
S.E. equation
0.023136
0.060816
0.038134
0.602635
Log likelihood
394.0993
234.6297
311.6410
-143.7914
Akaike AIC
394.2569
234.7872
311.7986
-143.6338
Schwarz SC
394.5016
235.0320
312.0433
-143.3891
Mean dependent
4.075405
-0.000154
-0.123119
-3.762357
S.D. dependent
1.911885
0.067029
1.409360
1.008271
Determinant Residual Covariance
4.92E-10


Log Likelihood
831.6832


Akaike Information Criteria
832.3135


Schwarz Criteria
833.2923



The responses of the variables studied to a standard deviation of innovations
(variation interval ± 2 standard deviations) are graphically represented, for a timespan of 10
monhs.


193


Quantitative Methods Inquires


Conclusions

Identifying a statistically strong model, capable of stable estimations for the money
demand function in Romania’s economy constitutes a prerequisite to the application of an
efficient monetary policy.
Obtaining by econometric means, the series of adjusted money demand, for which
the statistical stability tests are confirmed, allows for the formalization of the link between
the real-sector and monetary block, as well as the impact assessment of the levels of
monetary variables upon the economy.


194


Quantitative Methods Inquires
Bibliography

1. Antohi, D., Udrea, I. and Braun, H. Mecanismul de transmisie a politicii monetare in
Romania, Caiete studii BNR nr.13/2003
2. Boughton, J. M. Long run money demand in large industrial countries, International
Monetary Fund, 1991
3. Friedman, M. The optimum quantity of money and other essays, Aldine, Chicago, 1969
4. Sriram, S. S. A survey of recent empirical money demand studies, International Monetary
Fund, 2001
5. Treichel, V. Broad money demand and monetary policy in Tunisia, International Monetary
Fund, 1997


1 Codification of references:
[1]
Antohi, D., Udrea, I. and Braun, H. Mecanismul de transmisie a politicii monetare in Romania, Caiete
studii BNR nr.13/2003
[2] Boughton,
J.
M.
Long run money demand in large industrial countries, International Monetary Fund,
1991
[3] Friedman,
M.
The optimum quantity of money and other essays, Aldine, Chicago, 1969
[4]
Sriram, S. S. A survey of recent empirical money demand studies, International Monetary Fund, 2001
[5]
Treichel, V. Broad money demand and monetary policy in Tunisia, International Monetary Fund, 1997


195

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