Download Academia de Studii Economice Şcoala Doctorală de Finanţe

Bucharest University of Economics
Doctoral School of Finance and Banking
DOFIN
Policy Mechanism
Transmission Channels in Romania
Supervisor: Professor Dr. Moisă Altăr
MSc Student: Ion Săvulescu
Bucharest July 2008
Contents
•
•
•
•
The objectives of the dissertation paper
The actual stage of research in the field of
substantiating the monetary policy using VAR
econometric models
The theoretical substantiation of the transmission
channels for the monetary policy and the
justification of the methodology and techniques.
Utilised Data and processing methodology
The results I obtained
•
Conclusions
•
1. The objectives of the dissertation
paper
• The identification of the monetary
transmission mechanism main features in
Romania, using econometric models (VAR
methodology)
• Using the estimated VAR models (including
structural VAR) I pursued the identification of
the monetary policy transmission channels
and also of a way of modeling the money
demand
2. The actual stage of research in the field
of substantiating the monetary policy using
VAR econometric models
• During the evolution of the economic science the
formulation of the first transmission mechanism for the
monetary policy belongs to J. M. Keynes
Specification of a structural model of the effect of
monetary policy over the economic activity
• On the other side, the monetarists backed the second
approach, by specifying a model with reduced form
and the analysis of the relation between the levels of
the money supply and that of the economic activity,
the estimation correlation coefficient between the two
variables (Milton Friedman – the promoter)
3. The theoretical substantiation of the transmission
channels for the monetary policy and the
justification of the methodology and techniques
• Monetary policy leads to strong, rapid and
generalized effects over some variables like prices
and production, these actually being the main
objectives of this type of actions
Change in the
monetary policy
instrument
Alterations of the
financial assets prices
Deviations from the
equilibrium values of
production and
unemployment
Interest rates Exchange
rates
Alterations of the economic
agents’ and households’
behavior
Wages and prices adjustment
to a new equilibrium
• Many economists agree with the claim according to
which the effects of monetary policy over the
production begin to appear after some time and
are effects on a relatively short term, production
receding on long term to its natural level.
• The main monetary transmission channels are:
– The interest rate channel
– The exchange rate channel
– The assets’ prices channel
– The credit channel
– The expectations channel
4. Utilised Data and processing
methodology
Symbol
BZ
BZR
CRNG
Description (monthly)
Rezerv Money, in mil. Lei
Real BZ, CPI deflated, base 10:1990
Total credit to Non-Governments, in mil. Lei
CRNGR
CS
CSR
Real CRNG, CPI deflated, base 10:1990
Exchange rate (lei/euro)
Real exchange rate, CPI deflated, base 01:1990
DA
DAR
DPMML
Landing rate for non-bank customers
Real landing rate for non-bank customers
Monetary Policy Interest Rate
DPMMLR
DP
DPR
IPCL
Real Monetary Policy Interest Rate
Deposit rate to non-bank customers
Real DP, CPI deflated, base 10:1990
Inflation CPI
IPCX
Inflation, CPI, chain, base 10:1990
IPCXLOG Inflation, CPI, chain, in logs
IPPIX
IPPIL
PPI, base 10:1990
PPI chain, base 10:1990
IPPXLOG PPI in logs
M1
M1R
M1
Real M1, CPI deflated, base 10:1990
M2
M2R
SC
M2
Real M2, CPI deflated, base 10:1990
NBR’s Reference Rate
SCR
Real NBR’s Reference Rate
SMNB
Nominal gross average monthly wage
SMRB
Real gross average monthly wage
VPIX
VPIL
Industrial output variation rate, base 02:1990
Industrial output variation chain
Baza monetar a
BZR
50000
CRNG
16
CRNGR
200000
14
40000
5
50
160000
12
30000
CS
60
4
40
120000
10
3
30
8
20000
80000
2
20
6
10000
40000
4
0
2
92
94
96
98
00
02
04
06
08
0
92
94
96
98
CSR
00
02
04
06
08
0
92
94
96
DA
.007
1
10
98
00
02
04
06
08
0
92
94
96
98
DAR
120
00
02
04
06
08
100
.005
80
94
96
98
00
02
04
06
08
120
94
96
98
00
02
04
06
08
92
94
96
98
00
02
04
06
08
94
96
98
00
02
04
06
08
00
02
04
06
08
02
04
06
08
40000
5
10000
0
92
94
96
M2R
98
00
02
04
06
08
0
92
94
96
IPPIX
160000
08
80000
10
20000
98
06
120000
20
30000
104
96
04
M2
40000
108
94
00
50000
120000
92
98
160000
15
96
96
25
60000
112
100
94
M1R
30
160000
0
92
80000
116
40000
02
0
92
M1
90000
200000
80000
08
10
0
70000
240000
06
30
20
10
IPCL
280000
04
40
40
20
IPCX
124
02
50
40
92
320000
08
60
20
92
06
70
80
30
0
04
60
40
.001
02
100
70
50
20
00
80
60
.002
98
90
60
.003
96
DPR
120
80
.004
94
DP
100
90
.006
92
98
00
02
04
06
08
0
92
94
96
SMNB
500000
98
00
02
04
06
08
92
94
96
SMRB
2000
98
00
VPIX
.60
90
.55
120000
400000
1600
300000
1200
200000
800
100000
400
0
0
80
.50
.45
80000
70
.40
40000
60
.35
.30
50
.25
0
92
94
96
98
00
02
04
06
08
02
04
06
08
92
94
96
98
00
02
04
06
08
.20
92
94
96
98
00
02
04
06
08
SC
90
80
70
60
50
40
30
20
10
0
92
94
96
98
00
Graficul 1. Principalele variabile utilizate
40
92
94
96
98
00
02
04
06
08
92
94
96
98
00
The used methodology
• I studied the seasonality using U.S. Census Bureau X-12
monthly seasonal adjustment method
• I also studied the stationarity of the series
• Granger-causality test
• Regression equation of the industrial production’s
variation (VIP) on the main variables in the analyzed
group
• “Limited” model of unrestricted VAR, with three endogenous
variables (VPIX, CRNGR and M1R) and four exogenous (BZR, DP, IPCX and
SMRB), with a number of 6 lags.
• 14 unrestricted VAR models with 7 variables and six lags
• 1 SVAR model
• Cointegration test
5. The results I obtained
5.1 Granger causality tests
I applied Granger causality tests in two steps. In the first stage
I applied the test on the entire set presented in section 4.
Using the results from this step, I selected a group of 12
variables on which I applied the Granger causality test
again.
By processing the results from the second step (the
elimination of the pairs with the probability of the hypothesis
over the threshold of 5%, the grouping of the remaining
variables in “cause” variables), I was able to make the
following observations regarding the causality relations
between the studied variables:
Pairwise Granger Causality Tests
Date: 07/06/08 Time: 15:27
Sample: 1992M03 2008M03
Lags: 12
Null Hypothesis:
BZR does not Granger Cause CRNG
BZR does not Granger Cause DAR
BZR does not Granger Cause DPR
BZR does not Granger Cause M1R
BZR does not Granger Cause M2R
BZR does not Granger Cause SMRB
BZR does not Granger Cause VPIX
CRNG does not Granger Cause BZR
CRNG does not Granger Cause IPCX
CRNG does not Granger Cause IPPIX
CRNG does not Granger Cause M1R
CRNG does not Granger Cause M2R
CRNG does not Granger Cause SMRB
CSR does not Granger Cause BZR
CSR does not Granger Cause DAR
CSR does not Granger Cause DPR
CSR does not Granger Cause IPCX
CSR does not Granger Cause M1R
CSR does not Granger Cause SC
DAR does not Granger Cause CSR
DAR does not Granger Cause SMRB
DPR does not Granger Cause DAR
DPR does not Granger Cause SMRB
DPR does not Granger Cause VPIX
Obs
181
181
181
181
181
181
F-Statistic
2.49008
2.23737
2.72811
2.27109
3.43178
3.6423
2.96209
5.16196
2.79881
2.92074
5.78281
12.7235
2.20223
2.93625
12.4125
6.82007
2.19557
2.47458
4.02731
3.38365
2.63241
4.1105
1.98978
1.97617
Probability
0.00518
0.01242
0.00223
0.01107
0.00017
8.00E-05
0.00096
3.00E-07
0.00173
0.00111
3.20E-08
7.00E-18
0.01399
0.00105
1.70E-17
8.20E-10
0.01431
0.00547
1.90E-05
0.00021
0.00313
1.40E-05
0.02845
0.02975
IPCX does not Granger Cause CSR
IPCX does not Granger Cause DAR
IPCX does not Granger Cause DPR
IPCX does not Granger Cause IPPIX
IPCX does not Granger Cause M1R
IPCX does not Granger Cause M2R
IPCX does not Granger Cause SMRB
IPCX does not Granger Cause VPIX
IPPIX does not Granger Cause DAR
IPPIX does not Granger Cause M1R
IPPIX does not Granger Cause M2R
M1R does not Granger Cause BZR
M1R does not Granger Cause CRNG
M1R does not Granger Cause DAR
M1R does not Granger Cause DPR
M1R does not Granger Cause M2R
M1R does not Granger Cause SMRB
M1R does not Granger Cause VPIX
M2R does not Granger Cause BZR
M2R does not Granger Cause CRNG
M2R does not Granger Cause IPPIX
M2R does not Granger Cause M1R
M2R does not Granger Cause SMRB
M2R does not Granger Cause VPIX
SC does not Granger Cause BZR
SC does not Granger Cause CSR
SC does not Granger Cause DAR
SC does not Granger Cause DPR
SC does not Granger Cause IPCX
SC does not Granger Cause IPPIX
SC does not Granger Cause M1R
SC does not Granger Cause SMRB
SMRB does not Granger Cause BZR
SMRB does not Granger Cause CRNG
SMRB does not Granger Cause DAR
SMRB does not Granger Cause M1R
SMRB does not Granger Cause M2R
SMRB does not Granger Cause VPIX
VPIX does not Granger Cause BZR
VPIX does not Granger Cause M1R
VPIX does not Granger Cause M2R
VPIX does not Granger Cause SMRB
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
2.39828
4.35064
2.41814
1.92691
2.43348
2.16552
1.99519
3.19733
2.72082
4.04105
2.25571
2.78881
1.87764
2.70512
2.44637
2.11323
2.01778
2.83857
4.08758
5.39774
3.09058
4.96576
4.29948
2.30382
3.35934
3.15948
4.35228
8.05429
2.35047
2.8107
3.89362
3.11061
5.60025
2.04068
1.82184
4.16026
2.4768
8.9938
4.36888
4.65015
2.5065
7.30686
0.00713
5.80E-06
0.00666
0.03492
0.00631
0.01585
0.02795
0.00041
0.00229
1.80E-05
0.01166
0.00179
0.04093
0.00242
0.00603
0.01889
0.02595
0.0015
1.50E-05
1.30E-07
0.0006
6.10E-07
7.10E-06
0.00989
0.00023
0.00047
5.80E-06
1.20E-11
0.00842
0.00166
3.20E-05
0.00056
6.10E-08
0.02406
0.04888
1.20E-05
0.00542
5.60E-13
5.50E-06
1.90E-06
0.00489
1.50E-10
• There are causality relations between the majority of the
variables in the study (BZR, M1R, M2R) and the nongovernmental credit, which seems to indicate the presence of the
credit channel in the monetary policy mechanism;
• The exchange rate has an influence well showed by the test’s
results both on the monetary variables (BZR, M1R, SC) and on
the inflation (IPCX) and over the interest rates in use at the
commercial banks (DAR, DPR); this seems to indicate the
channel of the exchange rate is working;
• The inflation (IPCX) influences all the monetary variables
(except for the NBR’s Reference rate) and also the variables of
the economy’s real sector (VPIX, SMBR, IPPX), the commercial
banks’ interest rates (DAR and DPR) and exchange rate (CSR). I
believe that this observation can be considered a modest
argument for the appositeness of choosing the inflation targeting
as an objective of the monetary policy.
5.2 Regression equation of the industrial
production’s variation (VIP) on the main
variables in the analyzed group
Dependent Variable: VPIX
Method: Least Squares
Date: 06/25/08 Time: 20:09
Sample (adjusted): 1992M04 2008M03
Included observations: 192 after adjustments
Variable
VPIX(-1)
CRNGR
BZR
M1R
M2R
DAR
DPR
CSR
IPCL
IPPIL
SMNBR
SC
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Coefficient
0.762754
0.751775
0.000775
-1.31416
-0.000161
0.065833
-0.107886
523.9632
0.579743
-0.402256
-20.96156
-0.034324
0.789787
0.776941
3.98288
2855.399
-531.5855
Std. Error
t-Statistic
12.6985
0.060067
3.760628
0.199907
2.046523
0.000378
-3.335537
0.393988
-1.777298
9.07E-05
0.900476
0.073109
-1.486853
0.07256
0.538811
972.4439
0.670553
0.864574
-0.468318
0.858937
-1.329873
15.76208
-0.742087
0.046253
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Durbin-Watson stat
Prob.
0
0.0002
0.0422
0.001
0.0772
0.3691
0.1388
0.5907
0.5034
0.6401
0.1852
0.459
63.56927
8.433096
5.662349
5.865942
1.999396
I resumed the regression, eliminating the
variables that had an insignificant influence
Dependent Variable: VPIX
Method: Least Squares
Date: 06/25/08 Time: 20:36
Sample (adjusted): 1992M04 2008M03
Included observations: 192 after adjustments
Variable
Coefficient
Std. Error
C
VPIX(-1)
CRNGR
CRNGR(-1)
M1R
M1R(-1)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
13.60179
0.73549
1.642965
-1.071888
-2.057781
1.25941
0.788229
0.782536
3.932606
2876.562
-532.2944
2.0062
t-Statistic
2.817385
4.827809
0.052183
14.0945
0.498958
3.29279
0.503744
-2.127843
0.574712
-3.580543
0.603247
2.087721
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
Prob.
0
0
0.0012
0.0347
0.0004
0.0382
63.56927
8.433096
5.607233
5.70903
138.4615
0
From these regressions I was able to make
the following observations:
-There is an important influence of the industrial
production’s previous value, of the real governmental
credit and of the monetary supply in a restricted sense
and a smaller influence of the real monetary base
I
- In the case of the credit and in that of the monetary
supply, the contemporaneous has an inverted
direction in comparison to that of the previous period.
5.3 Unrestricted VAR model
On the ground of previous results I computed an
unrestricted VAR on three endogenous variables (VPIX
– Industrial output variation rate, CRNGR – Total real
credit to Governments and M1R – Real M1) and four
exogenous variables (BZR – Real Reserve Money, DP –
Real deposit rate to non-bank customers, IPCX –
Inflation, CPI, chain and SMRB – Real gross average
monthly wage) and with a number of six lags.
I have, thereby, obtained the graphs representing the
endogenous variables’ responses to to the shocks of a
standard error of each of these.
Response to Nonfactorized One S.D. Innovations ± 2 S.E.
Response of VPIX to VPIX
Response of VPIX to CRNGR
Response of VPIX to M1R
5
5
5
4
4
4
3
3
3
2
2
2
1
1
1
0
0
0
-1
-1
2
4
6
8
10
12
14
16
18
-1
20
2
Response of CRNGR to VPIX
.6
.5
.4
.3
.2
.1
.0
-.1
-.2
4
6
8
10
12
14
16
6
8
10
12
14
16
18
20
18
20
.7
.6
.6
.5
.5
.4
.4
.3
.3
.2
.2
.1
.1
.0
.0
-.1
-.1
-.2
4
6
8
10
12
14
16
18
20
6
8
10
12
14
16
18
20
20
2
4
6
8
10
12
14
16
18
20
18
20
.0
-.1
-.2
18
.1
.0
-.1
16
.2
.1
.0
14
.3
.2
.1
12
.4
.3
.2
10
.5
.4
.3
8
Response of M1R to M1R
.5
.4
6
-.2
2
Response of M1R to CRNGR
.5
4
4
Response of CRNGR to M1R
.7
Response of M1R to VPIX
2
2
Response of CRNGR to CRNGR
.7
2
4
-.1
-.2
-.2
2
4
6
8
10
12
14
16
18
20
2
4
6
8
10
12
14
16
From these graphs we ca observe:
• The positive reaction of the industrial production’s
variation in response to an impulse on the nongovernmental credit as well as the fact that the
production’s stabilization is being done at a higher
level
• An impulse on the monetary supply leads, in the first
part, to a negative reaction of the industrial
production followed by waving movement where
the positive components are dominated and the
amplitude is declining. The shock is desorbed after 6
– 7 periods (months) the industrial production
reversing to the previous level.
14 unrestricted VAR models
Upon the estimation and analysis of a long series of
VAR models I kept 14 of those whose structure is
presented below. From among those I selected three
models that I presented in the thesis both as structure
and as the result of the usage of the functions impulseresponse and of the decomposition of that
option/variation.
Serie
CRNG
CRNGR
L_CRNGR
BZ
BZR
L_BZR_SA
M1
M1R
L_M1R_SA
M2
M2R
DA
DAR
DAR_SA
DP
DPR
CS
CSR
CSR_SA
L_CSR_SA
IPCX
IPCL
IPCL_SA
IPPIX
IPPIL
IPPILLOG
SMNB
SMRB
VPIX
VPIL
VPIX_SA
SC
VAR01
1
VAR03
VAR04 VAR041 VAR05 VAR051
1
1
1
1
1
1
1
1
1
1
1
1
1
1
VAR06
VAR61
VAR07
VAR08
VAR081
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
VAR08c VAR091
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
*
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
7
1
7
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
7
LL
AIC
SC
VAR02
1
7
7
7
1
7
7
7
7
7
7
1
7
7
-3,047.80 -1,781.55 -3,653.98 370.05 142.88 -398.55 1,012.63 -4154.13 -3367.71 -3,394.86
-9.84
84.05
-9.84 -818.78
35.81599 22.27326 42.29923 -0.664
1.766 7.48187
-7.611 47.64847 39.23751 39.5279 3.32451 2.320299 3.324514 12.0511
41.01686 27.47413 47.5001
4.658
7.088 12.6827 -2.4101 52.84935 44.43839 44.7288 8.52539 7.521175 8.525389 17.3729
Following, I will present one of the models I
used:
The variables:
L_CRNGR
L_BZR_SA
L_M1R_SA
DAR_SA
L_CSR_SA
IPCL_SA
VPIX_SA
Real CRNG, CPI deflated, base 10:1990, in logs
Real BZ, CPI deflated, base 10:1990, sesonall adjusted, in logs
Real M1, CPI deflated, base 10:1990, sesonall adjusted, in logs
Real landing rate for non-bank customers, sesonall adjusted
Real exchange rate, CPI deflated, base 01:1990, sesonall adjusted, in logs
Inflation CPI, sesonall adjusted
Industrial output variation rate, base 02:1990, sesonall adjusted
The graphs for all variables’ responses in the model to
the impulses coming from each of these are:
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of L_CRNGR to L_CRNGR
Response of L_CRNGR to L_BZR_SA
Response of L_CRNGR to L_M1R_SA
Response of L_CRNGR to DAR_SA
Response of L_CRNGR to L_CSR_SA
Response of L_CRNGR to IPCL_SA
Response of L_CRNGR to VPIL
.06
.06
.06
.06
.06
.06
.06
.04
.04
.04
.04
.04
.04
.04
.02
.02
.02
.02
.02
.02
.02
.00
.00
.00
.00
.00
.00
.00
-.02
-.02
-.02
-.02
-.02
-.02
-.02
-.04
-.04
-.04
-.04
-.04
-.04
-.04
-.06
-.06
1
2
3
4
5
6
7
8
9
10
Response of L_BZR_SA to L_CRNGR
-.06
1
2
3
4
5
6
7
8
9
10
-.06
1
2
3
4
5
6
7
8
9
10
Response of L_BZR_SA to L_BZR_SA Response of L_BZR_SA to L_M1R_SA
-.06
1
2
3
4
5
6
7
8
9
10
Response of L_BZR_SA to DAR_SA
-.06
1
2
3
4
5
6
7
8
9
10
Response of L_BZR_SA to L_CSR_SA
-.06
1
2
3
4
5
6
7
8
9
10
1
Response of L_BZR_SA to IPCL_SA
.06
.06
.06
.06
.06
.06
.06
.04
.04
.04
.04
.04
.04
.04
.02
.02
.02
.02
.02
.02
.00
.00
.00
.00
.00
.00
.00
-.02
-.02
-.02
-.02
-.02
-.02
-.02
-.04
-.04
-.04
-.04
-.04
-.04
-.04
-.06
-.06
1
2
3
4
5
6
7
8
9
10
Response of L_M1R_SA to L_CRNGR
-.06
1
2
3
4
5
6
7
8
9
10
-.06
1
2
3
4
5
6
7
8
9
10
Response of L_M1R_SA to L_BZR_SA Response of L_M1R_SA to L_M1R_SA
-.06
1
2
3
4
5
6
7
8
9
10
Response of L_M1R_SA to DAR_SA
2
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
1
.08
.08
.08
.08
.08
.04
.04
.04
.04
.04
.04
.04
.00
.00
.00
.00
.00
.00
.00
-.04
-.04
-.04
-.04
-.04
-.04
-.04
-.08
2
3
4
5
6
7
8
9
10
Response of DAR_SA to L_CRNGR
-.08
1
2
3
4
5
6
7
8
9
10
Response of DAR_SA to L_BZR_SA
-.08
1
2
3
4
5
6
7
8
9
10
Response of DAR_SA to L_M1R_SA
-.08
1
2
3
4
5
6
7
8
9
10
Response of DAR_SA to DAR_SA
-.08
1
2
3
4
5
6
7
8
9
10
Response of DAR_SA to L_CSR_SA
2
3
4
5
6
7
8
9
10
1
Response of DAR_SA to IPCL_SA
5
5
5
5
5
5
4
4
4
4
4
4
3
3
3
3
3
3
3
2
2
2
2
2
2
2
1
1
1
1
1
1
0
0
0
0
0
0
0
-1
-1
-1
-1
-1
-1
-1
-2
-2
-2
-2
-2
-2
-2
-3
1
2
3
4
5
6
7
8
9
-3
10
1
2
3
4
5
6
7
8
9
10
-3
1
2
3
4
5
6
7
8
9
10
Response of L_CSR_SA to L_BZR_SA Response of L_CSR_SA to L_M1R_SA
-3
1
2
3
4
5
6
7
8
9
10
Response of L_CSR_SA to DAR_SA
2
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
1
.05
.05
.05
.05
.05
.04
.04
.04
.04
.04
.04
.04
.03
.03
.03
.03
.03
.03
.03
.02
.02
.02
.02
.02
.02
.01
.01
.01
.01
.01
.01
.00
.00
.00
.00
.00
-.01
-.01
-.01
-.01
-.01
-.02
-.02
-.02
-.02
-.02
-.02
-.02
-.03
-.03
-.03
-.03
-.03
-.03
-.03
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Response of IPCL_SA to L_BZR_SA
1
2
3
4
5
6
7
8
9
10
Response of IPCL_SA to L_M1R_SA
1
2
3
4
5
6
7
8
9
10
Response of IPCL_SA to DAR_SA
1
2
3
4
5
6
7
8
9
10
Response of IPCL_SA to L_CSR_SA
1
2
3
4
5
6
7
8
9
10
1
Response of IPCL_SA to IPCL_SA
1.6
1.6
1.6
1.6
1.6
1.6
1.2
1.2
1.2
1.2
1.2
1.2
1.2
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.4
0.4
0.4
0.4
0.4
0.4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.4
-0.4
-0.4
-0.4
-0.4
-0.4
-0.4
-0.8
-0.8
-0.8
-0.8
-0.8
-0.8
-0.8
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Response of VPIL to L_BZR_SA
1
2
3
4
5
6
7
8
9
10
1
Response of VPIL to L_M1R_SA
2
3
4
5
6
7
8
9
10
Response of VPIL to DAR_SA
1
2
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
1
Response of VPIL to IPCL_SA
8
8
8
8
8
8
6
6
6
6
6
6
6
4
4
4
4
4
4
4
2
2
2
2
2
2
0
0
0
0
0
0
0
-2
-2
-2
-2
-2
-2
-2
-4
-4
-4
-4
-4
-4
-4
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
6
7
8
9
10
2
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
2
3
4
5
6
7
8
9
10
Response of VPIL to VPIL
8
1
5
0.4
1
Response of VPIL to L_CSR_SA
4
Response of IPCL_SA to VPIL
1.6
Response of VPIL to L_CRNGR
3
.01
.00
-.01
3
2
.02
.00
-.01
2
10
Response of L_CSR_SA to VPIL
.05
1
9
-3
1
Response of L_CSR_SA to IPCL_SA
.05
Response of IPCL_SA to L_CRNGR
8
1
-3
1
Response of L_CSR_SA to L_CSR_SA
7
Response of DAR_SA to VPIL
4
-3
6
-.08
1
5
Response of L_CSR_SA to L_CRNGR
5
Response of L_M1R_SA to VPIL
.08
1
4
-.06
1
Response of L_M1R_SA to IPCL_SA
.08
-.08
3
.02
-.06
1
Response of L_M1R_SA to L_CSR_SA
2
Response of L_BZR_SA to VPIL
2
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
From the Analysis of these graphs it can be inferred that
• The positive variation of the non-governmental credit to the
shocks on the monetary policy variables (monetary base and
monetary supply in a restricted sense). The stabilization of the
credit following a shock on the monetary supply is being
achieved at a higher level of the credit;
• An ample response of all the model’s variables to the shock on
the consumer price index (inflation);
• The shock on inflation has a negative effect on the variation of
the industrial production and its stabilization is being achieved
at a lower level;
• The consumer price index is quite sensible to the shocks on the
majority of the analyzed variables;
• A persistent waving movement (more than 20 periods), with
dominant positive components, is caused by the exchange rate
on the inflation index (IPC).
The responses of the consumer price index to one
standard deviation shocks on the variables in model
1 are portrayed in the following graph.
Response of IPCL_SA to Cholesky
One S.D. Innovations
1.2
0.8
0.4
0.0
-0.4
-0.8
2
4
6
L_CRNGR
L_BZR_SA
L_M1R_SA
8
10
12
14
DAR_SA
L_CSR_SA
IPCL_SA
16
18
20
VPIX_SA
The varince decomposition of the consumer price index
is:
Variance Decomposition of IPCL_SA
80
70
60
50
40
30
20
10
0
2
4
6
L_CRNGR
L_BZR_SA
L_M1R_SA
8
10
12
14
DAR_SA
L_CSR_SA
IPCL_SA
16
18
20
VPIX_SA
The response of the model’s variables to a standard
deviation shock on the consumer price index
Response to Choles ky One S.D. Innovations
Response of L_BZR_SA to IPCL_SA
Response of L_M1R_SA to IPCL_SA
.000
.000
-.004
-.004
-.008
-.008
Response of L_CRNGR to IPCL_SA
.00
-.01
-.012
-.012
-.02
-.016
-.016
-.020
-.020
-.03
-.024
-.024
-.028
-.028
-.04
-.032
-.032
-.05
-.036
2
4
6
8
10
12
14
16
18
20
2
Response of DAR_SA to IPCL_SA
4
6
8
10
12
14
16
18
20
2
Response of L_CSR_SA to IPCL_SA
4
6
8
10
12
14
16
18
20
18
20
Response of VPIX_SA to IPCL_SA
3.5
.006
.1
3.0
.004
2.5
.002
2.0
.000
-.3
1.5
-.002
-.4
1.0
-.004
0.5
-.006
0.0
-.008
.0
-.1
-.2
-.5
-.6
2
4
6
8
10
12
14
16
18
20
18
20
Response of IPCL_SA to IPCL_SA
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
2
4
6
8
10
12
14
16
-.7
-.8
2
4
6
8
10
12
14
16
18
20
2
4
6
8
10
12
14
16
5.4 Structural VAR (SVAR)
The main purpose in the estimation of the SVAR models
is to obtain an un-recursive orthogonalization of the
error terms for the impulse-response analysis. This
alternative to the recursive Colesky orthogonalization
requires the user to impose sufficient restriction in order
to identify the orthogonal components of the error
terms
In this paper I made an SVAR model, only with short
term restrictions, using the following VAR model:
CRNGR
BZR
M1R
DAR
IPCL
VPIX_SA
CSR
Total credit to Non-Governments, in mil. Lei
Real BZ, CPI deflated, base 10:1990
Real M1, CPI deflated, base 10:1990
Real landing rate for non-bank customers
Inflation CPI
Industrial output variation rate, base 02:1990, seasonall adjusted
Real exchange rate, CPI deflated, base 01:1990
I identified and introduced 70 restrictions by
fixing 70 elements of the matrixes that needed
to be estimated (the structural form matrixes of
the autoregressive vector). Using the
procedure “Estimate Structural Factorization”
in EViews, I estimated the SVAR model.
Analyzing the impulse-response function from
the estimated model, one can notice an
ample effect on the system’s variables
determined by the shock on the exchange
rate.
Response to Structural One S.D. Innovations
Response of CRNGR to Shock7
Response of BZR to Shock7
40
10
0
0
-10
-40
-20
-80
-30
-120
-40
-160
-50
-200
-60
2
4
6
8
10
12
14
16
18
20
2
4
Response of DAR to Shock7
6
8
10
12
14
16
18
20
Response of IPCL to Shock7
1000
300
250
800
200
600
150
100
400
50
200
0
0
-50
2
4
6
8
10
12
14
16
18
20
2
4
Response of VPIX_SA to Shock7
6
8
10
12
14
16
18
20
Response of CSR to Shock7
40
.036
.032
0
.028
.024
-40
.020
.016
-80
.012
.008
-120
2
4
6
8
10
12
14
16
18
20
.004
2
4
6
8
10
12
14
16
18
20
5.5 Cointegration tests
The purpose of these tests is to determine whether a group of nonstationary variables are cointegrated. If for a group of time series, of
which one or more are not stationary, a stationary linear
combination is identified, one can say the series of the group are
cointegrated. The stationary linear combination is called
cointegration equation and can be viewed as a long-term equilibrium
relation between the variables. The presence of the cointegration
relation is the basis for the Vector Error Correction (VEC) models.
I applied the cointegration test for the unrestricted VAR model
presented in section 5.4.
The results of the test show the following:
• According to the “trace” test:
– For a 5% significance level there are 4 cointegration
equations;
– For a 1% significance level there are 3 cointegration
equations;
• According to the “max eigenvalue” test, there are 3
cointegration equations at both the 1% and the 5%
levels
A synthesis for the results of the cointegration test is
showed below.
Date: 06/17/08 Time: 16:42
VAR08
Sample(adjusted): 1992:10 2008:03
Included observations: 186 after adjusting endpoints
Trend assumption: Linear deterministic trend (restricted)
Series: CRNGR BZR M1R DAR CSR IPCL VPIX
Lags interval (in first differences): 1 to 6
Unrestricted Cointegration Rank Test
Hypothesized
No. of CE(s)
Eigenvalue
Trace
Statistic
5 Percent
Critical Value
1 Percent
Critical Value
None **
At most 1 **
At most 2 **
At most 3 *
At most 4
At most 5
At most 6
0.396702
0.270263
0.219395
0.134637
0.110125
0.072924
0.023711
265.8123
171.8182
113.2149
67.14521
40.24854
18.54718
4.463352
146.76
114.90
87.31
62.99
42.44
25.32
12.25
158.49
124.75
96.58
70.05
48.45
30.45
16.26
*(**) denotes rejection of the hypothesis at the 5%(1%) level
Trace test indicates 4 cointegrating equation(s) at the 5% level
Trace test indicates 3 cointegrating equation(s) at the 1% level
Hypothesized
No. of CE(s)
None **
At most 1 **
At most 2 **
At most 3
At most 4
At most 5
At most 6
Eigenvalue
Max-Eigen
Statistic
5 Percent
Critical Value
1 Percent
Critical Value
0.396702
0.270263
0.219395
0.134637
0.110125
0.072924
0.023711
93.99410
58.60326
46.06969
26.89667
21.70136
14.08383
4.463352
49.42
43.97
37.52
31.46
25.54
18.96
12.25
54.71
49.51
42.36
36.65
30.34
23.65
16.26
*(**) denotes rejection of the hypothesis at the 5%(1%) level
Max eigenvalue test indicates 3 cointegrating equation(s) at both 5% and 1% level
6. Conclusions
• Bank credits affect the actual activity in the economy (represented
in the study herein by the industrial production and the average
gross salary). On its part, the credit is affected on a short term by
the monetary policy variables. I consider these elements to be a
proof of the existence and functioning of the bank credit channel
as one of the main mechanism for the monetary policy diffusion in
Romania.
• Consumer price index (the inflation) is a variable very sensitive
to the shocks and influences of the monetary variables, but also, of
the macroeconomic variables. I consider this modest emphasize on
the inflation manifestation on the current Romanian economy,
accomplished by the study carried out in this paper, to be a
justification for the appropriateness of aiming to choose target
inflation as goal of the monetary policy in Romania.
• The exchange rate is another channel through which the
monetary policy has been diffused in the Romanian economy
during the analyzed period. Exchange rate variation is also highly
influenced by the domestic innovations and the monetary shocks.
Considering the domestic innovations as main indicator of the
forecasts, we notice that these represent the main determinant, on a
short term, of the exchange rate evolution.
• The test performed on the patterns developed and presented in the
paper confirm the assessment of many Economists, according to
whom, the monetary policy effect on production occurs after a
long period of time and are effects on a relatively short term, the
production retrieving its natural level on a long term
Thank you for your attention!