Transcript Slide 1
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 Typically, in the long run:
M d
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 Real broad money* Real output* Exchange rate* Exchange rate depreciation Inflation Deposit interest rate T-bills interest rate
Seasonally adjusted variables
ADF test I(1) C I(1) C I(1) C T I(1) C or I(0) C I(1) C or I(0) C I(1) C T I(1) C T PP test I(1) C I(1) C I(1) C T I(0) C I(1) C or I(0) C I(1) C T I(1) C T Real broad money* Inflation Real output* I(1) C I(1) C or I(0) C I(1) C I(1) C I(1) C or I(0) C I(1) C 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).
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The results for seasonally adjusted data:
I II III I II III Coef.
1.39
1.33
1.46
Output SE 0.49
0.19
0.21
t 2.78
6.73
6.73
Depreciation Coef.
SE t -0.46
0.09
-4.98
Deposit interest rate Coef.
SE t 3.52
2.92
3.57
0.95
0.4
0.29
3.69
7.16
12.05
Exchange rate Coef.
SE t T-bills interest rate Coef.
SE t -2.13
-0.65
-1.27
0.55
0.19
0.15
-3.85
-3.36
-7.97
Speed of adjustment Coef.
SE t -0.04
0.01
-2.43
Coef.
Inflation SE -0.48
0.18
t -2.65
-0.31
-0.47
0.07
0.05
-4.31
-9.00
RMSE 4/ Stat.
0.02
Dyn.
0.09
-0.10
-0.11
0.02
0.02
-4.17
-4.54
0.03
0.02
0.12
0.08
-0.34
0.2
1.69
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 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.
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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.
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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 3 2 1 0 -1 -2 6 4 2 0 -2 -4 1996 1996 1997 1997 1998 1998 1999 ECM1 1999 ECM1dummy 2000 2000 2001 2001 4 3 2 1 0 -1 -2 1996 1997 4 3 2 1 0 -1 -2 1996 1997 1998 1999 ECM3 1998 1999 ECM2 2000 2001 2000 2001 14
The stability of parameters in the long run equilibrium:
In figures are presented the CUSUM tests, recursive residuals, N step forecast test and the recursive coefficients.
.12
20 .00
.12
.08
.08
10 .04
.04
.04
I.
.00
-.04
0 .08
.00
-.04
-10 .12
-.08
-.08
-.12
-.12
-20 1999 2000 Recursive Residuals 2001 ± 2 S.E.
1999 CUSUM 2000 2001 5% Significance 1999 N-Step Probability 2000 2001 Recursive Residuals
II.
III.
.00
-.02
.04
.02
.06
-.04
-.06
.12
.08
.04
.00
-.04
-.08
-.12
1998 1999 Recursive Residuals 2000 ± 2 S.E.
2001 99:01 99:07 00:01 00:07 01:01 01:07 Recursive Residuals ± 2 S.E.
20 10 0 -10 -20 1998 1999 CUSUM 2000 5% Significance 2001 -5 -10 -15 -20 20 15 10 5 0 99:01 99:07 CUSUM 00:01 00:07 01:01 5% Significance 01:07 .00
.04
.08
.12
1998 1999 N-Step Probability .15
.10
.05
.00
-.05
-.10
2000 2001 Recursive Residuals -.15
.00
.04
.08
.12
.08
.04
.00
-.04
99:01 99:07 00:01 00:07 01:01 01:07 -.08
N-Step Probability Recursive Residuals 15
Recursive coefficients of the short run unrestricted ECM (model I):
.12
.08
.04
.00
-.04
-.08
-.12
99:01 99:07 00:01 ECM 1_1 00:07 01:01 ± 2 S.E.
01:07 0.8
0.4
0.0
-0.4
-0.8
-1.2
99:01 99:07 00:01 LYRIBF_SA_1 00:07 01:01 ± 2 S.E.
01:07 .0
-.2
-.4
-.6
-.8
.8
.6
.4
.2
99:01 99:07 00:01 DDP_3 00:07 01:01 ± 2 S.E.
01:07 .1
.0
-.1
-.2
.4
.3
.2
99:01 99:07 00:01 DDT S_4 00:07 01:01 ± 2 S.E.
01:07 .12
.08
.04
.00
-.04
-.08
.4
.2
.0
-.2
-.4
-.6
0.8
0.4
0.0
-0.4
-0.8
-1.2
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
99:01 99:07 00:01 DLM 2R_SA_1 00:07 01:01 ± 2 S.E.
01:07 99:01 99:07 00:01 LYRIBF_SA_2 00:07 01:01 ± 2 S.E.
01:07 99:01 99:07 00:01 DDP_4 00:07 01:01 ± 2 S.E.
01:07 99:01 99:07 00:01 DP_SA_1 00:07 01:01 ± 2 S.E.
01:07 .10
.08
.06
.04
.02
.00
-.02
-.04
-.06
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
.2
.1
.0
-.1
-.2
-.3
99:01 99:07 00:01 DLM 2R_SA_2 00:07 01:01 ± 2 S.E.
01:07 1.6
1.2
0.8
0.4
0.0
-0.4
-0.8
99:01 99:07 00:01 LYRIBF_SA_3 00:07 01:01 ± 2 S.E.
01:07 99:01 99:07 00:01 DDT S_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 .08
.06
.04
.02
.00
-.02
-.04
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
.2
.1
.0
-.1
-.2
-.3
99:01 99:07 00:01 DLM 2R_SA_3 00:07 01:01 ± 2 S.E.
01:07 0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
99:01 99:07 00:01 LYRIBF_SA_4 00:07 01:01 ± 2 S.E.
01:07 99:01 99:07 00:01 DDT S_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 .08
.06
.04
.02
.00
-.02
-.04
.0
-.1
-.2
-.3
.3
.2
.1
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
0.8
0.4
0.0
-0.4
-0.8
-1.2
-1.6
-2.0
99:01 99:07 00:01 DLM 2R_SA_4 00:07 01:01 ± 2 S.E.
01:07 99:01 99:07 00:01 DDP_1 00:07 01:01 ± 2 S.E.
01:07 99:01 99:07 00:01 DDT S_3 00:07 01:01 ± 2 S.E.
01:07 99:01 99:07 00:01 DP_SA_4 00:07 01:01 ± 2 S.E.
01:07
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 in 1997 cannot be performed.
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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:
2 ( 5 )
5.73[0.34].
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:
LM
2
R
C
5
1 6
1
ji
V j
,
t
i
1
ECM t
1
t
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 ECM1_1 DLM2R_SA_1 DDTS DP_SA C R-squared Adjusted R-squared S.E. of regression Sum squared resid Coefficient -0.013942
0.313701
-0.026417
-0.014734
-0.005467
0.667695
0.646256
0.019311
0.023122
Std. Error 0.004046
0.115123
0.011747
0.001866
0.002521
t-Statistic -3.446164
2.724918
-2.248838
-7.894747
-2.168777
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Prob. 0.0010
0.0083
0.0281
0.0000
0.0339
-0.007540
0.032469
-4.984542
-4.820013
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. Log likelihood 171.9822
F-statistic 31.14393
Durbin-Watson stat 2.144583
Prob(F-statistic) 0.000000
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
1.2
.08
.06
.01
.04
0.8
.04
.02
.00
0.4
.00
-.01
.00
-.02
-.02
0.0
-.04
-.03
-.04
-0.4
-.06
-.04
-.08
-.05
-0.8
-.08
1997 1998 1999 2000 2001 1997 1998 1999 2000 2001 1997 1998 1999 2000 2001 1997 1998 1999 2000 Recursive Residuals ± 2 S.E.
ECM1_1 ± 2 S.E.
DLM2R_SA_1 ± 2 S.E.
DDTS ± 2 S.E.
2001 30 .01
.02
20 .00
.01
10 -.01
.00
0 -.02
-10 -.01
-.03
-20 -.02
-.04
-30 -.05
-.03
1997 1998 CUSUM 1999 2000 5% Significance 2001 1997 1998 DP_SA 1999 2000 ± 2 S.E.
2001 1997 1998 C 1999 ± 2 S.E.
2000 2001 .08
.00
.04
.04
.08
.12
.00
-.04
1997 1998 N-Step Probability 1999 2000 2001 Recursive Residuals -.08
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
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 6 5 8 7 1 0 4 3 2 -0.04
-0.02
0.00
0.02
0.04
Series: Residuals Sample 1996:03 2001:09 Observations 67 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 0.976923
Probability 0.613570
8 7 6 5 4 3 2 1 0 -0.06
-0.04
-0.02
0.00
0.02
0.04
Series: Residuals Sample 1996:06 2001:09 Observations 64 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 1.865176
Probability 0.393534
21
Forecasting ability
9.7
9.6
9.5
9.4
9.3
9.2
9.1
9.0
8.9
1996 •
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
1997 1998 1999 2000 2001 Forecast: LM2R_SAF Actual: LM2R_SA Forecast sample: 1996:01 2002:03 Adjusted sample: 1996:03 2002:03 Included observations: 73 Root Mean Squared Error Mean Absolute Error Variance Proportion Covariance Proportion 0.018253
0.014995
Mean Abs. Percent Error 0.162360
Theil Inequality Coefficient 0.000991
Bias Proportion 0.002481
0.002368
0.995151
9.2
9.1
9.0
8.9
9.7
9.6
9.5
9.4
9.3
1996 1997 1998 actual 1999 2000 fitted 2001 9.18
9.16
9.14
9.12
9.10
9.08
9.06
9.04
2001:10 actual dinamic 2002:01 static 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
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 t d
k
*
y t
t
*
DP
t
*
DTS
t
*
p t
t
t
0
1
t
1
u t
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. Residual from Kalman filter
4 3 2 1 0 -1 -2 -3 -4 1996 1997 1998 1999 residual 2000 2001 24
The deposit rate elasticity of the money demand 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.
Filtered State DP Estimate 40 30 20 70 60 50 10 0 -10 -20 1996 1997 1998 1999 2000 2001 DP ± 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 his policy.
26
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2.
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