bucharest university of economics doctoral school of finance and banking dofin
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Bucharest University of EconomicsDoctoral School of Finance and Banking
DOFIN
Policy Mechanism
Transmission Channels in Romania
Supervisor: Professor Dr. Moisă AltărMSc 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
Interest rates Exchange rates
Alterations of the financial assets prices
Alterations of the economic agents’ and households’
behavior
Deviations from the equilibrium values of
production and unemployment
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 Description (monthly)
BZ Rezerv Money, in mil. Lei
BZR Real BZ, CPI deflated, base 10:1990
CRNG Total credit to Non-Governments, in mil. Lei
CRNGR Real CRNG, CPI deflated, base 10:1990
CS Exchange rate (lei/euro)
CSR Real exchange rate, CPI deflated, base 01:1990
DA Landing rate for non-bank customers
DAR Real landing rate for non-bank customers
DPMML Monetary Policy Interest Rate
DPMMLR Real Monetary Policy Interest Rate
DP Deposit rate to non-bank customers
DPR Real DP, CPI deflated, base 10:1990
IPCL Inflation CPI
IPCX Inflation, CPI, chain, base 10:1990
IPCXLOG Inflation, CPI, chain, in logs IPPIX PPI, base 10:1990
IPPIL PPI chain, base 10:1990
IPPXLOG PPI in logs M1 M1
M1R Real M1, CPI deflated, base 10:1990
M2 M2
M2R Real M2, CPI deflated, base 10:1990
SC NBR’s Reference Rate
SCR Real NBR’s Reference Rate
SMNB Nominal gross average monthly wage
SMRB Real gross average monthly wage
VPIX Industrial output variation rate, base 02:1990
VPIL Industrial output variation chain
0
10000
20000
30000
40000
50000
92 94 96 98 00 02 04 06 08
Baza monetara
2
4
6
8
10
12
14
16
92 94 96 98 00 02 04 06 08
BZR
0
40000
80000
120000
160000
200000
92 94 96 98 00 02 04 06 08
CRNG
0
10
20
30
40
50
60
92 94 96 98 00 02 04 06 08
CRNGR
0
1
2
3
4
5
92 94 96 98 00 02 04 06 08
CS
.001
.002
.003
.004
.005
.006
.007
92 94 96 98 00 02 04 06 08
CSR
0
20
40
60
80
100
120
92 94 96 98 00 02 04 06 08
DA
10
20
30
40
50
60
70
80
90
100
92 94 96 98 00 02 04 06 08
DAR
0
20
40
60
80
100
120
92 94 96 98 00 02 04 06 08
DP
0
10
20
30
40
50
60
70
80
90
92 94 96 98 00 02 04 06 08
DPR
0
40000
80000
120000
160000
200000
240000
280000
320000
92 94 96 98 00 02 04 06 08
IPCX
96
100
104
108
112
116
120
124
92 94 96 98 00 02 04 06 08
IPCL
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
92 94 96 98 00 02 04 06 08
M1
0
5
10
15
20
25
30
92 94 96 98 00 02 04 06 08
M1R
0
40000
80000
120000
160000
92 94 96 98 00 02 04 06 08
M2
0
40000
80000
120000
160000
92 94 96 98 00 02 04 06 08
M2R
0
100000
200000
300000
400000
500000
92 94 96 98 00 02 04 06 08
IPPIX
0
400
800
1200
1600
2000
92 94 96 98 00 02 04 06 08
SMNB
.20
.25
.30
.35
.40
.45
.50
.55
.60
92 94 96 98 00 02 04 06 08
SMRB
40
50
60
70
80
90
92 94 96 98 00 02 04 06 08
VPIX
0
10
20
30
40
50
60
70
80
90
92 94 96 98 00 02 04 06 08
SC
Graficul 1. Principalele variabile utilizate
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 testsI 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 TestsDate: 07/06/08 Time: 15:27Sample: 1992M03 2008M03Lags: 12
Null Hypothesis: Obs F-Statistic Probability BZR does not Granger Cause CRNG 181 2.49008 0.00518 BZR does not Granger Cause DAR 2.23737 0.01242 BZR does not Granger Cause DPR 2.72811 0.00223 BZR does not Granger Cause M1R 2.27109 0.01107 BZR does not Granger Cause M2R 3.43178 0.00017 BZR does not Granger Cause SMRB 3.6423 8.00E-05 BZR does not Granger Cause VPIX 2.96209 0.00096 CRNG does not Granger Cause BZR 5.16196 3.00E-07 CRNG does not Granger Cause IPCX 2.79881 0.00173 CRNG does not Granger Cause IPPIX 2.92074 0.00111 CRNG does not Granger Cause M1R 5.78281 3.20E-08 CRNG does not Granger Cause M2R 12.7235 7.00E-18 CRNG does not Granger Cause SMRB 2.20223 0.01399 CSR does not Granger Cause BZR 181 2.93625 0.00105 CSR does not Granger Cause DAR 181 12.4125 1.70E-17 CSR does not Granger Cause DPR 181 6.82007 8.20E-10 CSR does not Granger Cause IPCX 2.19557 0.01431 CSR does not Granger Cause M1R 181 2.47458 0.00547 CSR does not Granger Cause SC 4.02731 1.90E-05 DAR does not Granger Cause CSR 3.38365 0.00021 DAR does not Granger Cause SMRB 2.63241 0.00313 DPR does not Granger Cause DAR 181 4.1105 1.40E-05 DPR does not Granger Cause SMRB 1.98978 0.02845 DPR does not Granger Cause VPIX 1.97617 0.02975
IPCX does not Granger Cause CSR 181 2.39828 0.00713 IPCX does not Granger Cause DAR 181 4.35064 5.80E-06 IPCX does not Granger Cause DPR 181 2.41814 0.00666 IPCX does not Granger Cause IPPIX 1.92691 0.03492 IPCX does not Granger Cause M1R 181 2.43348 0.00631 IPCX does not Granger Cause M2R 181 2.16552 0.01585 IPCX does not Granger Cause SMRB 1.99519 0.02795 IPCX does not Granger Cause VPIX 3.19733 0.00041 IPPIX does not Granger Cause DAR 181 2.72082 0.00229 IPPIX does not Granger Cause M1R 181 4.04105 1.80E-05 IPPIX does not Granger Cause M2R 181 2.25571 0.01166 M1R does not Granger Cause BZR 181 2.78881 0.00179 M1R does not Granger Cause CRNG 181 1.87764 0.04093 M1R does not Granger Cause DAR 2.70512 0.00242 M1R does not Granger Cause DPR 2.44637 0.00603 M1R does not Granger Cause M2R 2.11323 0.01889 M1R does not Granger Cause SMRB 2.01778 0.02595 M1R does not Granger Cause VPIX 2.83857 0.0015 M2R does not Granger Cause BZR 181 4.08758 1.50E-05 M2R does not Granger Cause CRNG 181 5.39774 1.30E-07 M2R does not Granger Cause IPPIX 3.09058 0.0006 M2R does not Granger Cause M1R 181 4.96576 6.10E-07 M2R does not Granger Cause SMRB 4.29948 7.10E-06 M2R does not Granger Cause VPIX 2.30382 0.00989 SC does not Granger Cause BZR 181 3.35934 0.00023 SC does not Granger Cause CSR 181 3.15948 0.00047 SC does not Granger Cause DAR 181 4.35228 5.80E-06 SC does not Granger Cause DPR 181 8.05429 1.20E-11 SC does not Granger Cause IPCX 181 2.35047 0.00842 SC does not Granger Cause IPPIX 181 2.8107 0.00166 SC does not Granger Cause M1R 181 3.89362 3.20E-05 SC does not Granger Cause SMRB 181 3.11061 0.00056 SMRB does not Granger Cause BZR 181 5.60025 6.10E-08 SMRB does not Granger Cause CRNG 181 2.04068 0.02406 SMRB does not Granger Cause DAR 181 1.82184 0.04888 SMRB does not Granger Cause M1R 181 4.16026 1.20E-05 SMRB does not Granger Cause M2R 181 2.4768 0.00542 SMRB does not Granger Cause VPIX 8.9938 5.60E-13 VPIX does not Granger Cause BZR 181 4.36888 5.50E-06 VPIX does not Granger Cause M1R 181 4.65015 1.90E-06 VPIX does not Granger Cause M2R 181 2.5065 0.00489 VPIX does not Granger Cause SMRB 181 7.30686 1.50E-10
• There are causality relations between the majority of the variables in the study (BZR, M1R, M2R) and the non-governmental 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
Variable Coefficient t-Statistic Prob.
VPIX(-1) 0.762754 12.6985 0CRNGR 0.751775 3.760628 0.0002
BZR 0.000775 2.046523 0.0422M1R -1.31416 -3.335537 0.001M2R -0.000161 -1.777298 0.0772DAR 0.065833 0.900476 0.3691DPR -0.107886 -1.486853 0.1388CSR 523.9632 0.538811 0.5907IPCL 0.579743 0.670553 0.5034IPPIL -0.402256 -0.468318 0.6401
SMNBR -20.96156 -1.329873 0.1852SC -0.034324 -0.742087 0.459
R-squared 0.789787 63.56927Adjusted R-squared 0.776941 8.433096S.E. of regression 3.98288 5.662349Sum squared resid 2855.399 5.865942Log likelihood -531.5855 1.999396 Durbin-Watson stat
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion
15.762080.046253
0.07256972.44390.8645740.858937
0.0003780.3939889.07E-050.073109
0.0600670.199907
Included observations: 192 after adjustments
Std. Error
Dependent Variable: VPIXMethod: Least SquaresDate: 06/25/08 Time: 20:09Sample (adjusted): 1992M04 2008M03
I resumed the regression, eliminating the variables that had an insignificant influence
Dependent Variable: VPIXMethod: Least SquaresDate: 06/25/08 Time: 20:36Sample (adjusted): 1992M04 2008M03Included observations: 192 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
C 13.60179 2.817385 4.827809 0VPIX(-1) 0.73549 0.052183 14.0945 0CRNGR 1.642965 0.498958 3.29279 0.0012CRNGR(-1) -1.071888 0.503744 -2.127843 0.0347M1R -2.057781 0.574712 -3.580543 0.0004M1R(-1) 1.25941 0.603247 2.087721 0.0382R-squared 0.788229 Mean dependent var 63.56927Adjusted R-squared 0.782536 S.D. dependent var 8.433096S.E. of regression 3.932606 Akaike info criterion 5.607233Sum squared resid 2876.562 Schwarz criterion 5.70903Log likelihood -532.2944 F-statistic 138.4615Durbin-Watson stat 2.0062 Prob(F-statistic) 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 modelOn 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.
-1
0
1
2
3
4
5
2 4 6 8 10 12 14 16 18 20
Response of VPIX to VPIX
-1
0
1
2
3
4
5
2 4 6 8 10 12 14 16 18 20
Response of VPIX to CRNGR
-1
0
1
2
3
4
5
2 4 6 8 10 12 14 16 18 20
Response of VPIX to M1R
-.2
-.1
.0
.1
.2
.3
.4
.5
.6
.7
2 4 6 8 10 12 14 16 18 20
Response of CRNGR to VPIX
-.2
-.1
.0
.1
.2
.3
.4
.5
.6
.7
2 4 6 8 10 12 14 16 18 20
Response of CRNGR to CRNGR
-.2
-.1
.0
.1
.2
.3
.4
.5
.6
.7
2 4 6 8 10 12 14 16 18 20
Response of CRNGR to M1R
-.2
-.1
.0
.1
.2
.3
.4
.5
2 4 6 8 10 12 14 16 18 20
Response of M1R to VPIX
-.2
-.1
.0
.1
.2
.3
.4
.5
2 4 6 8 10 12 14 16 18 20
Response of M1R to CRNGR
-.2
-.1
.0
.1
.2
.3
.4
.5
2 4 6 8 10 12 14 16 18 20
Response of M1R to M1R
Response to Nonfactorized One S.D. Innovations ± 2 S.E.
From these graphs we ca observe:
• The positive reaction of the industrial production’s variation in response to an impulse on the non-governmental 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 impulse-response and of the decomposition of that option/variation.
Serie VAR01 VAR02 VAR03 VAR04 VAR041 VAR05 VAR051 VAR06 VAR61 VAR07 VAR08 VAR081 VAR08c VAR091CRNG 1CRNGR 1 1 1 1 1 1 1 1 1 1L_CRNGR 1 1BZBZR 1 1 1 1 1 1 1 1 1L_BZR_SA 1 1M1 1 1M1R 1 1 1 1 1 1L_M1R_SA 1 1M2 1 1 1M2R 1 1 1DA 1 1DAR 1 1 1 1 1 1 1 1 *DAR_SA 1 1DP 1DPR 1 1CS 1 1 1CSR 1 1 1 1 1CSR_SA 1L_CSR_SA 1 1IPCX 1 1IPCL 1 1 1 1 1 1IPCL_SA 1 1IPPIXIPPIL 1IPPILLOG 1 1SMNB 1SMRB 1 1 1 1 1VPIX 1 1 1 1 1 1 1VPIL 1VPIX_SA 1 1 1 1SC 1
7 7 7 7 7 7 7 7 7 7 7 7 7 7
LL -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 -818.78AIC 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 12.0511SC 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 Real CRNG, CPI deflated, base 10:1990, in logs L_BZR_SA Real BZ, CPI deflated, base 10:1990, sesonall adjusted, in logs L_M1R_SA Real M1, CPI deflated, base 10:1990, sesonall adjusted, in logs DAR_SA Real landing rate for non-bank customers, sesonall adjusted L_CSR_SA Real exchange rate, CPI deflated, base 01:1990, sesonall adjusted, in logs IPCL_SA Inflation CPI, sesonall adjusted VPIX_SA 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:
-.06
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of L_CRNGR to L_CRNGR
-.06
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of L_CRNGR to L_BZR_SA
-.06
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of L_CRNGR to L_M1R_SA
-.06
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of L_CRNGR to DAR_SA
-.06
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of L_CRNGR to L_CSR_SA
-.06
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of L_CRNGR to IPCL_SA
-.06
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of L_CRNGR to VPIL
-.06
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of L_BZR_SA to L_CRNGR
-.06
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of L_BZR_SA to L_BZR_SA
-.06
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of L_BZR_SA to L_M1R_SA
-.06
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of L_BZR_SA to DAR_SA
-.06
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of L_BZR_SA to L_CSR_SA
-.06
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of L_BZR_SA to IPCL_SA
-.06
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
Response of L_BZR_SA to VPIL
-.08
-.04
.00
.04
.08
1 2 3 4 5 6 7 8 9 10
Response of L_M1R_SA to L_CRNGR
-.08
-.04
.00
.04
.08
1 2 3 4 5 6 7 8 9 10
Response of L_M1R_SA to L_BZR_SA
-.08
-.04
.00
.04
.08
1 2 3 4 5 6 7 8 9 10
Response of L_M1R_SA to L_M1R_SA
-.08
-.04
.00
.04
.08
1 2 3 4 5 6 7 8 9 10
Response of L_M1R_SA to DAR_SA
-.08
-.04
.00
.04
.08
1 2 3 4 5 6 7 8 9 10
Response of L_M1R_SA to L_CSR_SA
-.08
-.04
.00
.04
.08
1 2 3 4 5 6 7 8 9 10
Response of L_M1R_SA to IPCL_SA
-.08
-.04
.00
.04
.08
1 2 3 4 5 6 7 8 9 10
Response of L_M1R_SA to VPIL
-3
-2
-1
0
1
2
3
4
5
1 2 3 4 5 6 7 8 9 10
Response of DAR_SA to L_CRNGR
-3
-2
-1
0
1
2
3
4
5
1 2 3 4 5 6 7 8 9 10
Response of DAR_SA to L_BZR_SA
-3
-2
-1
0
1
2
3
4
5
1 2 3 4 5 6 7 8 9 10
Response of DAR_SA to L_M1R_SA
-3
-2
-1
0
1
2
3
4
5
1 2 3 4 5 6 7 8 9 10
Response of DAR_SA to DAR_SA
-3
-2
-1
0
1
2
3
4
5
1 2 3 4 5 6 7 8 9 10
Response of DAR_SA to L_CSR_SA
-3
-2
-1
0
1
2
3
4
5
1 2 3 4 5 6 7 8 9 10
Response of DAR_SA to IPCL_SA
-3
-2
-1
0
1
2
3
4
5
1 2 3 4 5 6 7 8 9 10
Response of DAR_SA to VPIL
-.03
-.02
-.01
.00
.01
.02
.03
.04
.05
1 2 3 4 5 6 7 8 9 10
Response of L_CSR_SA to L_CRNGR
-.03
-.02
-.01
.00
.01
.02
.03
.04
.05
1 2 3 4 5 6 7 8 9 10
Response of L_CSR_SA to L_BZR_SA
-.03
-.02
-.01
.00
.01
.02
.03
.04
.05
1 2 3 4 5 6 7 8 9 10
Response of L_CSR_SA to L_M1R_SA
-.03
-.02
-.01
.00
.01
.02
.03
.04
.05
1 2 3 4 5 6 7 8 9 10
Response of L_CSR_SA to DAR_SA
-.03
-.02
-.01
.00
.01
.02
.03
.04
.05
1 2 3 4 5 6 7 8 9 10
Response of L_CSR_SA to L_CSR_SA
-.03
-.02
-.01
.00
.01
.02
.03
.04
.05
1 2 3 4 5 6 7 8 9 10
Response of L_CSR_SA to IPCL_SA
-.03
-.02
-.01
.00
.01
.02
.03
.04
.05
1 2 3 4 5 6 7 8 9 10
Response of L_CSR_SA to VPIL
-0.8
-0.4
0.0
0.4
0.8
1.2
1.6
1 2 3 4 5 6 7 8 9 10
Response of IPCL_SA to L_CRNGR
-0.8
-0.4
0.0
0.4
0.8
1.2
1.6
1 2 3 4 5 6 7 8 9 10
Response of IPCL_SA to L_BZR_SA
-0.8
-0.4
0.0
0.4
0.8
1.2
1.6
1 2 3 4 5 6 7 8 9 10
Response of IPCL_SA to L_M1R_SA
-0.8
-0.4
0.0
0.4
0.8
1.2
1.6
1 2 3 4 5 6 7 8 9 10
Response of IPCL_SA to DAR_SA
-0.8
-0.4
0.0
0.4
0.8
1.2
1.6
1 2 3 4 5 6 7 8 9 10
Response of IPCL_SA to L_CSR_SA
-0.8
-0.4
0.0
0.4
0.8
1.2
1.6
1 2 3 4 5 6 7 8 9 10
Response of IPCL_SA to IPCL_SA
-0.8
-0.4
0.0
0.4
0.8
1.2
1.6
1 2 3 4 5 6 7 8 9 10
Response of IPCL_SA to VPIL
-4
-2
0
2
4
6
8
1 2 3 4 5 6 7 8 9 10
Response of VPIL to L_CRNGR
-4
-2
0
2
4
6
8
1 2 3 4 5 6 7 8 9 10
Response of VPIL to L_BZR_SA
-4
-2
0
2
4
6
8
1 2 3 4 5 6 7 8 9 10
Response of VPIL to L_M1R_SA
-4
-2
0
2
4
6
8
1 2 3 4 5 6 7 8 9 10
Response of VPIL to DAR_SA
-4
-2
0
2
4
6
8
1 2 3 4 5 6 7 8 9 10
Response of VPIL to L_CSR_SA
-4
-2
0
2
4
6
8
1 2 3 4 5 6 7 8 9 10
Response of VPIL to IPCL_SA
-4
-2
0
2
4
6
8
1 2 3 4 5 6 7 8 9 10
Response of VPIL to VPIL
Response to Cholesky One S.D. Innovations ± 2 S.E.
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.
-0.8
-0.4
0.0
0.4
0.8
1.2
2 4 6 8 10 12 14 16 18 20
L_CRNGRL_BZR_SAL_M1R_SA
DAR_SAL_CSR_SAIPCL_SA
VPIX_SA
Response of IPCL_SA to CholeskyOne S.D. Innovations
The varince decomposition of the consumer price index is:
0
10
20
30
40
50
60
70
80
2 4 6 8 10 12 14 16 18 20
L_CRNGRL_BZR_SAL_M1R_SA
DAR_SAL_CSR_SAIPCL_SA
VPIX_SA
Variance Decomposition of IPCL_SA
The response of the model’s variables to a standard deviation shock on the consumer price index
-.032
-.028
-.024
-.020
-.016
-.012
-.008
-.004
.000
2 4 6 8 10 12 14 16 18 20
Response of L_BZR_SA to IPCL_SA
-.036
-.032
-.028
-.024
-.020
-.016
-.012
-.008
-.004
.000
2 4 6 8 10 12 14 16 18 20
Response of L_M1R_SA to IPCL_SA
-.05
-.04
-.03
-.02
-.01
.00
2 4 6 8 10 12 14 16 18 20
Response of L_CRNGR to IPCL_SA
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
2 4 6 8 10 12 14 16 18 20
Response of DAR_SA to IPCL_SA
-.008
-.006
-.004
-.002
.000
.002
.004
.006
2 4 6 8 10 12 14 16 18 20
Response of L_CSR_SA to IPCL_SA
-.8
-.7
-.6
-.5
-.4
-.3
-.2
-.1
.0
.1
2 4 6 8 10 12 14 16 18 20
Response of VPIX_SA to IPCL_SA
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
2 4 6 8 10 12 14 16 18 20
Response of IPCL_SA to IPCL_SA
Response to Cholesky One S.D. Innovations
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 Total credit to Non-Governments, in mil. Lei BZR Real BZ, CPI deflated, base 10:1990 M1R Real M1, CPI deflated, base 10:1990 DAR Real landing rate for non-bank customers IPCL Inflation CPI VPIX_SA Industrial output variation rate, base 02:1990, seasonall adjusted CSR 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.
-200
-160
-120
-80
-40
0
40
2 4 6 8 10 12 14 16 18 20
Response of CRNGR to Shock7
-60
-50
-40
-30
-20
-10
0
10
2 4 6 8 10 12 14 16 18 20
Response of BZR to Shock7
0
200
400
600
800
1000
2 4 6 8 10 12 14 16 18 20
Response of DAR to Shock7
-50
0
50
100
150
200
250
300
2 4 6 8 10 12 14 16 18 20
Response of IPCL to Shock7
-120
-80
-40
0
40
2 4 6 8 10 12 14 16 18 20
Response of VPIX_SA to Shock7
.004
.008
.012
.016
.020
.024
.028
.032
.036
2 4 6 8 10 12 14 16 18 20
Response of CSR to Shock7
Response to Structural One S.D. Innovations
5.5 Cointegration tests
The purpose of these tests is to determine whether a group of non-stationary 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 Trace 5 Percent 1 Percent No. of CE(s) Eigenvalue Statistic Critical Value Critical Value
None ** 0.396702 265.8123 146.76 158.49 At most 1 ** 0.270263 171.8182 114.90 124.75 At most 2 ** 0.219395 113.2149 87.31 96.58 At most 3 * 0.134637 67.14521 62.99 70.05 At most 4 0.110125 40.24854 42.44 48.45 At most 5 0.072924 18.54718 25.32 30.45 At most 6 0.023711 4.463352 12.25 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 Max-Eigen 5 Percent 1 Percent No. of CE(s) Eigenvalue Statistic Critical Value Critical Value
None ** 0.396702 93.99410 49.42 54.71 At most 1 ** 0.270263 58.60326 43.97 49.51 At most 2 ** 0.219395 46.06969 37.52 42.36
At most 3 0.134637 26.89667 31.46 36.65 At most 4 0.110125 21.70136 25.54 30.34 At most 5 0.072924 14.08383 18.96 23.65 At most 6 0.023711 4.463352 12.25 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!