THE ACADEMY OF ECONOMIC STUDIES BUCHAREST
DOCTORAL SCHOOL OF FINANCE AND BANKING
DISSERTATION PAPER
Money Demand in Romania
Student: DUMITRU IONUŢ
Supervisor: PhD. Professor MOISĂ ALTĂR
BUCHAREST, JUNE 2002
1
STRUCTURE:
The role of the demand for money in the
transmission mechanism of monetary policy
Basic theoretical approaches to examination of
the money demand
 Money demand estimates in Eastern Europe
Money demand modelling in Romania
Conclusions
2
Basic theoretical approaches to examination of the money demand
1. Quantity theory
 “Equation of exchange” –Irving Fischer’s equation
 “Cambridge approach or cash balance approach”
2. Keynesian theory
3. Neo-Keynesian theory – Baumol and Tobin
4. Post-Keynesian theories
5. Modern monetarist approach - Milton Friedman
3
MONEY DEMAND ESTIMATES IN EASTERN EUROPE
Klacek and Smidkova (1995) estimated the long-run demand for broad
and narrow money in the Czech Republic
Van Aarle and Budina (1996) estimated money demand and
specifically the effect of currency substitution using the portfolio
balance approach for Poland, Hungary, Romania, and Bulgaria
Arlt, Guba, Radkovsky, Sojka and Stiller (2001) estimated money
demand for Czech Republic in period 1994-2000.
Erwin Nijsse and Elmer Sterken (1996) estimated a household money
demand function for Poland from 1969 to 1995.
Antoni Chawluk (2000) analysed the same household money demand
and consumption for Poland.
4
MONEY DEMAND MODELLING IN ROMANIA
d
Typically, in the long run:
M  f ( y; r; x )
y is a scale variable measuring the level of economic activity
r – a vector of opportunity cost variables
x is a vector of other variables (including dummy variables)
Monthly data between January 1996 and March 2002
Money balances are measured as real Lei M2 (M2 less foreign
currency deposits - in order to point out currency substitution).
Why M2?– because NBR uses M2 as intermediary target.
As a scale variable - the real industrial production index
(deflated by the consumer price index - CPI) as a proxy for GDP
which is not calculated monthly in Romania.
5
Opportunity costs:
Deposit interest rate for non-bank clients – measures the
rate of return on lei time deposits (proxy for return of M2).
T-bills interest rate – measures the rate of return on assets
outside of M2.
Expected inflation rate – proxied by current inflation rate –
captures the return on real assets and the substitution
between monetary assets and real assets (goods).
Expected depreciation of the lei-dollar exchange rate captures the return on holding US dollars and currency
substitution. The actual depreciation was used as proxy for
the expected rate.
6
Three specifications (models) are analysed
I. Money demand= f (output, deposit interest rate, T-bills
interest rate, inflation) – closed economy model
II. Money demand= f (output, deposit interest rate, T-bills
interest rate, inflation, depreciation of exchange rate) open economy model
III. Money demand= f (output, deposit interest rate, T-bills
interest rate, exchange rate) - open economy model
REMARK: As many of the series exhibit regular seasonal
patterns, it is necessary to take account of the seasonal
factors in the estimation. This is done in two ways:
1) using seasonal adjusted data through Tramo-Seats procedure
(Eviews 4.0)
2) using raw series with monthly seasonal dummies (centred 7
dummy)
UNIT ROOT TESTS
Variable
ADF test
PP test
Real broad money*
I(1) C
I(1) C
Real output*
I(1) C
I(1) C
I(1) C T
I(1) C T
Exchange rate depreciation
I(1) C or I(0) C
I(0) C
Inflation
I(1) C or I(0) C
I(1) C or I(0) C
Deposit interest rate
I(1) C T
I(1) C T
T-bills interest rate
I(1) C T
I(1) C T
I(1) C
I(1) C
I(1) C or I(0) C
I(1) C or I(0) C
I(1) C
I(1) C
Exchange rate*
Seasonally adjusted variables
Real broad money*
Inflation
Real output*
The cointegration technique is appropriate to estimate the long run
money demand. Table suggests that none of the variables is integrated
of order 2 (I(2)) or more. Inflation and depreciation of exchange rate is
probably I(0). This does not mean that they should be excluded from
cointegrating vector (Dickey and Rossana(1994), and Enders(2000)).8
REMARKS:
I have used multivariate Johansen procedure for cointegration.
The number of lags included in cointegration tests was
determined by estimating a VAR with variables of interest and
using criteria like LR, FPE, AIC, SC and HQ. If the optimum lag in
VAR is p, then the VEC is estimated with p-1 lags.
For each cointegration test I have obtained one cointegrating
vector at 1 or 5%.
Data through end-September 2001 were used for estimations,
with the remaining observations reserved for out-of-sample
forecasting (6 months).
9
The results for seasonally adjusted data:
Output
Deposit interest rate
T-bills interest rate
Inflation
Coef.
SE
t
Coef.
SE
t
Coef.
SE
t
Coef.
SE
t
I
1.39
0.49
2.78
3.52
0.95
3.69
-2.13
0.55
-3.85
-0.48
0.18
-2.65
II
1.33
0.19
6.73
2.92
0.4
7.16
-0.65
0.19
-3.36
-0.31
0.07
-4.31
III
1.46
0.21
6.73
3.57
0.29
12.05
-1.27
0.15
-7.97
-0.47
0.05
-9.00
Depreciation
Coef.
SE
Exchange rate
t
Coef.
SE
Speed of adjustment
t
I
II
III
-0.46
0.09
-4.98
-0.34
0.2
1.69
RMSE 4/
Coef.
SE
t
Stat.
Dyn.
-0.04
0.01
-2.43
0.02
0.09
-0.10
0.02
-4.17
0.03
0.12
-0.11
0.02
-4.54
0.02
0.08
All the estimated coefficients are statistically significant at 1%.
Estimates for each of the models have the anticipated signs
(according with economic theory) and there is relatively little variation in
the estimates of key parameters.
As magnitude, the estimated coefficients are similar with those
obtained in other empirical works performed in other countries from10
Central and Eastern Europe
Discussion on estimated coefficients
 The output elasticity in the long run relationship is greater than one
as was expected for broad money aggregate. Output decrease in
Romania, especially in the first half of the last decade, was
accompanied by a demonetization.
 Like magnitude, income elasticity does not differ significantly from 1:
 2 (1)  2.28[0.12]), which is consistent with the quantity theory of money.
 The differences in size between interest rates coefficients in models I
and II may be due to the fact that multi-collinearity problem becomes
more acute in the case of model II due to the close correlation between
exchange rate depreciation and inflation.
 Deposit interest rate semi-elasticity of money demand is positive and
has an opposite sign to the T-bills interest rate semi-elasticity.
 The exchange rate depreciation coefficient has a negative sign
(consistent with economic theory) which indicates the existence of a
currency substitution in Romania.
11
 The coefficient of depreciation is relatively small. This is explained
by the fact that on average, the national currency appreciated in real
terms excepting the 1997 and 1999 shocks, and the deposit interest
rate was on average greater than the yield for foreign exchange
deposits, which was very obvious since 2001.
 The inflation semi-elasticity of money demand is negative, which
involves a substitution between monetary assets and real assets.
This is not surprising for a country like Romania in which financial
assets outside broad money are limited and the agents hold
significant quantities of real assets.
 The coefficients representing speed of adjustment indicate
relatively rapid adjustment of real money demand to disequilibria.
The negative sign of speed of adjustment suggests that if in the
previous month the money demand exceeded the long term level, in
the current month money demand would decrease.
12
Weak-exogeneity tests
Weak exogeneity hypothesis is accepted both separately (for
output, deposit interest rate, inflation, depreciation and exchange
rate) and jointly. It is rejected for broad money and T-bills.
The deposit interest rate weak exogeneity suggests that it is
determined outside the system (it is not caused by money demand,
but it determines money demand). The T-bills interest rate is not
weak exogenous and it reacts to the money demand disequilibria
from the long term level.
The relationship between inflation and broad money is from
inflation to broad money and not vice versa, the inflation being
weak exogenous for the money demand. It is not the rise in broad
money which generates inflation, but it is the broad money which is
accommodated to inflation as this monetary aggregate rises to
reach the equilibrium. Consequently, we can state that inflation is
not a monetary phenomenon.
13
REMARK:
The estimates using unadjusted data (with seasonally centred
dummy – as Johansen suggested) were not consistent in economical
and econometrical terms unless in the case of closed economy model,
the other models leading to statistically insignificant coefficients.
The unrestricted cointegrating relation:
 The figures show that the
deviation of money demand from
its long term level for the three
models with seasonally adjusted
data and model I with unadjusted
data is stationary, so we can use it
in an error correction mechanism.
This deviation is relatively small,
excepting 1997 when in the first
three months there was an obvious
monetary overhang, and in the next
two months a shortfall occurred
due to pressures made by NBR to
rise the interest rates, after which
the situation came back to normal.
4
4
3
3
2
2
1
1
0
0
-1
-1
-2
-2
1996
1997
1998
1999
2000
1996
2001
1997
1998
1999
2000
2001
2000
2001
ECM3
ECM1
6
4
3
4
2
2
1
0
0
-2
-1
-4
-2
1996
1997
1998
1999
ECM1dummy
2000
2001
1996
1997
1998
1999
ECM2
14
The stability of parameters in the long run equilibrium:
 In figures are presented the CUSUM tests, recursive residuals, Nstep forecast test and the recursive coefficients.
20
.12
.12
.00
.08
.08
10
.04
.04
I.
.00
0
.00
.04
.08
-.04
-.04
-10
.12
-.08
-.08
-.12
-20
-.12
1999
2000
1999
2001
Recursive Residuals
2000
CUSUM
± 2 S.E.
.12
1999
2001
2000
N-Step Probability
5% Significance
20
.00
10
.04
0
.08
2001
Recursive Residuals
.15
.10
.08
II.
.05
.04
.00
.00
-.05
-.04
-10
.12
-.10
-.08
-.15
-20
1998
-.12
1998
1999
2000
Recursive Residuals
2001
1999
CUSUM
2000
2001
1998
5% Significance
1999
N-Step Probability
2000
2001
Recursive Residuals
± 2 S.E.
20
.06
.08
.00
15
.04
10
III.
.04
.04
.02
5
0
.00
.00
.08
-5
-.02
-10
-.04
-.04
.12
-15
-.08
-20
-.06
99:01
99:07
00:01
00:07
Recursive Residuals
01:01
± 2 S.E.
01:07
99:01
99:07
00:01
CUSUM
00:07
01:01
5% Significance
01:07
99:01
99:07
00:01
N-Step Probability
00:07
01:01
01:07
Recursive Residuals
15
Recursive coefficients of the short run unrestricted ECM (model I):
.12
2.5
2.0
.08
1.5
.04
1.5
1.5
1.0
1.0
0.5
0.5
0.8
0.4
0.0
1.0
-0.4
.00
0.0
0.0
0.5
-.04
-0.8
0.0
-.08
-0.5
-.12
-1.0
99:01
99:07
00:01
ECM 1_1
00:07
01:01
01:07
± 2 S.E.
99:07
00:01
DLM 2R_SA_1
0.8
0.4
0.4
-0.5
-1.0
-1.0
-1.5
99:01
0.8
-0.5
00:07
01:01
01:07
0.0
-0.4
-0.4
-0.8
-0.8
± 2 S.E.
99:07
00:01
DLM 2R_SA_2
00:07
01:01
01:07
1.6
00:01
00:07
LYRIBF_SA_1
01:01
01:07
00:07
01:01
01:07
99:07
00:01
DLM 2R_SA_4
00:07
01:01
01:07
± 2 S.E.
1.5
0.4
1.0
0.5
0.0
-0.2
0.0
-0.4
-0.5
-0.6
-0.4
99:07
00:01
00:07
01:01
01:07
-1.0
-0.8
-0.8
99:01
LYRIBF_SA_2
-1.0
99:01
± 2 S.E.
99:07
00:01
00:07
LYRIBF_SA_3
01:01
01:07
-1.5
99:01
± 2 S.E.
99:07
00:01
LYRIBF_SA_4
.4
.2
.2
.2
.1
.1
.0
.0
.0
-.2
-.1
-.1
-.4
-.2
-.2
00:07
01:01
01:07
99:01
99:07
± 2 S.E.
00:01
DDP_1
00:07
01:01
01:07
± 2 S.E.
.3
.6
.4
99:01
± 2 S.E.
0.2
± 2 S.E.
.8
00:01
0.6
1.2
-1.2
99:07
99:07
DLM 2R_SA_3
0.0
99:01
-2.0
99:01
± 2 S.E.
0.4
-1.2
-1.6
-1.5
99:01
0.8
0.0
-1.2
.2
.1
.2
.0
.0
-.2
-.1
-.4
-.2
-.6
-.8
-.6
99:01
99:07
00:01
DDP_3
00:07
01:01
01:07
-.3
99:01
99:07
00:01
DDP_4
± 2 S.E.
.4
00:07
01:01
01:07
-.3
99:01
± 2 S.E.
99:07
00:01
DDT S_1
.12
00:07
01:01
01:07
± 2 S.E.
.10
.08
.04
.04
.1
.02
.00
.00
.0
-.04
-.08
99:01
99:07
00:01
DDT S_4
00:07
01:01
± 2 S.E.
01:07
00:07
01:01
01:07
99:01
± 2 S.E.
99:07
00:01
DDT S_3
.08
.08
.06
.06
.04
.04
.02
.02
00:07
01:01
01:07
± 2 S.E.
.00
.00
-.02
-.02
-.04
-.04
-.02
-.04
-.2
00:01
.06
.2
-.1
99:07
DDT S_2
.08
.3
-.3
99:01
-.06
99:01
99:07
00:01
DP_SA_1
00:07
01:01
± 2 S.E.
01:07
99:01
99:07
00:01
DP_SA_2
00:07
01:01
± 2 S.E.
01:07
99:01
99:07
00:01
DP_SA_3
00:07
01:01
± 2 S.E.
01:07
99:01
99:07
00:01
DP_SA_4
00:07
01:01
01:07
± 2 S.E.
 According to the tests performed, the estimated coefficients are
constant over time, although in 1997 there are some signs of
instability.
 Because of the insufficient number of observations until 1997:01 (12
observations), the models cannot be estimated separately for the two
sub-periods; this is the reason why a Chow test for a structural break
16
in 1997 cannot be performed.
REMARK:
 In order to test if the 1997 shock produced only a one time
jump in the determinants of money demand and left them
unmodified, we re-estimated the cointegrating relation for
the 1997:06-2001:09 period. For the long run we have
obtained parameters similar to those obtained for the entire
sample. For example, in model II, if we restrict all the long
run coefficients estimated for the 1997:06-2001:09 sample to
be the same with those estimated for the entire sample we
cannot reject the null hypothesis:
 (5)  5.73[0.34].
2
17
Short run error correction model (ECM)
o The estimated cointegrating equations include the factors
affecting the real money demand in the long run. In the short
run deviations from these relations can occur.
I estimate a short run parsimonious error correction model as
follows:
5
6
LM 2 R  C    ji V j ,t  i   1 ECMt 1   t
j 1 i 1
18
Using a general-to-specific methodology (D. Hendry) by eliminating
insignificant lags we obtain a parsimonious model:
Dependent Variable: D(LM2R_SA)
Method: Least Squares
Sample(adjusted): 1996:03 2001:09
Included observations: 67 after adjusting endpoints
Variable
Coefficient
Std. Error
t-Statistic
Prob.
ECM1_1
-0.013942
0.004046
-3.446164
0.0010
DLM2R_SA_1
0.313701
0.115123
2.724918
0.0083
DDTS
-0.026417
0.011747
-2.248838
0.0281
DP_SA
-0.014734
0.001866
-7.894747
0.0000
C
-0.005467
0.002521
-2.168777
0.0339
R-squared
0.667695
Mean dependent var
-0.007540
Adjusted R-squared
0.646256
S.D. dependent var
0.032469
S.E. of regression
0.019311
Akaike info criterion
-4.984542
Sum squared resid
0.023122
Schwarz criterion
-4.820013
Log likelihood
171.9822
F-statistic
31.14393
Durbin-Watson stat
2.144583
Prob(F-statistic)
0.000000
Like in the
unrestricted model,
in the restricted
model the error
correction term has a
negative sign and is
statistically
significant. In other
words, if there is an
excess of money in the
current month, in the
next month the agents
will reduce their money
holdings.
19
REMARK:
While the changes in output and in deposit interest rate do not affect the short
run changes in money demand, the money demand is influenced in the short
run by changes in T-bills interest rate and inflation.
In figures-the CUSUM tests, recursive residuals, N-step forecast test and the
recursive coefficients.
.08
.02
.06
1.2
.08
.01
.04
0.8
.02
.00
.00
-.01
-.02
-.02
-.04
-.03
.04
0.4
.00
0.0
-.04
-0.4
-.06
-.04
-.08
1997
1998
1999
Recursive Residuals
2000
2001
-.05
-0.8
1997
± 2 S.E.
1998
1999
ECM1_1
2000
2001
-.08
1997
± 2 S.E.
1998
1999
DLM2R_SA_1
2000
2001
± 2 S.E.
1997
1998
DDTS
1999
2000
± 2 S.E.
30
.01
.02
20
.00
.01
10
-.01
.00
0
-.02
-10
-.01
-.03
-20
-.02
-.04
-30
1997
1998
1999
2000
2001
-.05
-.03
1997
CUSUM
1998
1999
2000
2001
1997
1998
1999
2000
2001
5% Significance
DP_SA
± 2 S.E.
C
± 2 S.E.
.08
.00
.04
.04
.00
.08
-.04
.12
-.08
1997
1998
N-Step Probability
1999
2000
2001
Recursive Residuals
The coefficients from the parsimonious model
are more stable, the error bands decreasing
rapidly. After 1997, the coefficients from the
parsimonious ECM are virtually constant.
20
2001
Comparing the unrestricted model to parsimonious model, we
observe that the last is better in qualitative terms, and this is
confirmed by the normality tests of residuals.
Normality test of residual for
parsimonious ECM – model I
Normality test of residual for
unrestricted ECM – model I
9
8
Series: Residuals
Sample 1996:03 2001:09
Observations 67
8
7
6
5
4
3
2
1
0
-0.04
-0.02
0.00
0.02
0.04
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
-1.42E-18
-0.001275
0.046782
-0.043691
0.018717
0.179114
2.529239
Jarque-Bera
Probability
0.976923
0.613570
Series: Residuals
Sample 1996:06 2001:09
Observations 64
7
6
5
4
3
2
1
0
-0.06
-0.04
-0.02
0.00
0.02
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
-2.49E-18
0.000910
0.036619
-0.060841
0.018561
-0.386101
3.321162
Jarque-Bera
Probability
1.865176
0.393534
0.04
21
Forecasting ability
• Extending the model for the period October 2001-March 2002 we can
make a series of judgments regarding the stance of monetary policy
during this period, although this is subject to errors.
M2 actual vs. fitted model ECM parsimonious
9.7
Forecast: LM2R_SAF
Actual: LM2R_SA
Forecast sample: 1996:01 2002:03
Adjusted sample: 1996:03 2002:03
Included observations: 73
9.6
9.5
9.4
Root Mean Squared Error
0.018253
Mean Absolute Error
0.014995
Mean Abs. Percent Error
0.162360
Theil Inequality Coefficient
0.000991
Bias Proportion
0.002481
Variance Proportion
0.002368
Covariance Proportion
0.995151
9.3
9.2
9.1
9.0
9.7
9.18
9.6
9.16
9.5
9.14
9.4
9.12
9.3
9.2
9.10
9.1
9.08
9.0
9.06
8.9
1996
1997
1998
actual
8.9
1996
1997
1998
1999
2000
2001
1999
2000
fitted
2001
9.04
2001:10
actual
2002:01
dinamic
fitted
 forecasts suggest that the models provide a reasonable approximation of
the actual money demand process during 1996-2002
 the forecast for 2001:10-2002:03 can give us a clue about the stance of
monetary policy, indicating that on average the actual real money demand
was larger than the estimated money demand during this period, which
generated a monetary overhang, the monetary policy being more relaxed. 22
static
A state space model for the money demand in Romania
I estimated a state space model (time-varying parameters) model using
Kalman filters on the demand for money in Romania, following an
approach similar to Bomhoff (1991) and Stracca (2001).
Advantages:
 a time-varying parameters model can underline possible changes in
parameters over time.
 it can asses the impact of financial innovation onto money holdings.
Concretely, I specified a state space model where I explicitly
allowed the deposit interest rate elasticity of money demand to
vary. The model is specified as:
m  k   * yt   t * DP   t * DTS   t * pt   t
d
t
 t   0   1 t 1  ut
23
REMARKS:
The freely estimate for the output elasticity tends to give a value close
to 1.35. This is also close to the value of 1.39 estimated by Johansen
procedure.
The time-varying-parameters model is estimated by means of a
Kalman filter over the full sample period. This procedure requires
maximizing the likelihood function using an optimization algorithm (in
particular the BHHH – Berndt-Hall-Hall-Hausman algorithm).
The model appears to be well specified and the residual appears to be
stationary. Being the model specified in terms of the long run
relationship, the residual have some positive autocorrelation (Q statistic
Q(6)=14.35[0.00]), reflecting the existence of adjustments costs in
bringing monetary holdings to the desired equilibrium value.
4
3
2
Residual from
Kalman filter
1
0
-1
-2
-3
-4
1996
1997
1998
1999
2000
2001
residual
24
The deposit rate elasticity of the money demand
Filtered State DP Estimate
 A reduced but noticeable increase in
absolute value of the deposit interest rate
elasticity of money demand is visible
beginning from the first half of 2000. This
means that in the context of a progress
towards a more reduced and predictable
inflation, reduced and stable interest rates,
the preference for liquidity of the agents
increases.
 Another remark is related to the sharp
decline in the deposit interest rate elasticity
of money demand at the beginning of 1997.
This is due to the high level of deposit
interest rate (up to120-130% for term
deposits) for this period which caused the
money demand not to vary significantly at
one percent change in the deposit interest
rate.
70
60
50
40
30
20
10
0
-10
-20
1996
1997
1998
DP
1999
2000
2001
± 2 RMSE
25
CONCLUSIONS
 Cointegration analysis reveals that real broad money balances are
determined in the long run by real output, deposit interest rate, T-bills
interest rate, inflation and exchange rate depreciation.
In Romania, as we have seen, the inflation is weekly exogenous for
money demand, which means that inflation is not a monetary
phenomenon.
The opportunity cost variables carry the expected sign according to
economic theory.
The depreciation of exchange rate elasticity of the money demand
indicates currency substitution in Romania.
In the short run, the money demand is not influenced by changes in
output and deposit interest rate, but changes in T-bills interest rate and
inflation.
The demand for real money is stable for the period analyzed. Further
evidence in support of the stability of the model derives from its positive
forecasting performance.
The important findings are that both in the long and in the short-run
the demand for real M2 is stable providing justification for the monetary
authorities to use M2 aggregate like intermediary target in conducting
26
his policy.
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