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Determinants of inflation in romania
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Dissertation paperDeterminants of inflation in RomaniaStudent: Balan IrinaSupervisor: Professor Moisa AltarACADEMY OF ECONOMIC STUDIESDOCTORAL SCHOOL OF FINANCE AND BANKING
Contents Literature review The data Estimating the inflation rate CPI model PPI model Concluding remarks
LITERATURE REVIEW using cointegration and error-correction models Domac I. and Elbirt C. (1998) argue that the roots of Albanian inflation are rahter conventional: inflation is positively associated with money growth and exchange rates but negatively with real income Brada and Kutan (1999) - the Czech Republic, Hungary and Poland - import price changes play the most important role in explaining inflation dynamics, while nominal wage growth and money supply are quantitatively unimportant Nikolic M. (2000) estimated the determinants of Russian inflation in a single equation framework - money growth is a core determinant of Russian inflation Golinelli R. and Orsi R. (2002) - analysed the determinants of the inflation rate in the Czech Republic, Hungary and Poland and they found that the exchange rate is the main long term factor influencing domestic prices, and can be seen to be the common inflation-adjusting mechanism utilised in all three countries
Chart1
5.1
174.5
210.4
256.1
136.7
32.3
38.8
154.8
59.1
45.8
45.7
34.9
17.8
14
Romania
Figure 1: Anual inflation rate in Romania (%)
Sheet1
199019911992199319941995199619971998199920002001
Czech Republic10.856.711.220.8109.18.88.510.72.13.94.7
Hungary28.9352322.518.828.223.618.314.3109.89.2
Poland585.870.34335.332.227.819.914.911.87.310.15.5
Romania5.1174.5210.4256.1136.732.338.8154.859.145.845.734.9
19911992199319941995199619971998199920002001
Czech Republic56.711.220.8109.18.88.510.72.13.94.7
Hungary352322.518.828.223.618.314.3109.89.2
Poland70.34335.332.227.819.914.911.87.310.15.5
Romania174.5210.4256.1136.732.338.8154.859.145.845.734.9
19901991199219931994199519961997199819992000200120022003
Romania5.1174.5210.4256.1136.732.338.8154.859.145.845.734.917.814
Sheet1
0000
0000
0000
0000
0000
0000
0000
0000
0000
0000
0000
Czech Republic
Hungary
Poland
Romania
Sheet2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Romania
Anual inflation rate in Romania (%)
Sheet3
THE DATA (all variables in logarithms)
quarterly data sample: 1991:1 - 2003:4 source: National Bank of Romania, Romanian Institute of Statistics p - Consumer Price Index (fixed base 1996=100) p_ppi - Producer Price Index (fixed base 1996=100) m - broad money (M2) w - nominal wages y - industrial production index (fixed base 1996=100) e - nominal exchange rate (ROL/USD)
THE GOAL
to estimate the determinants of inflation
methods: Cointegration technique, VAR and VEC models
Estimating the inflation rate
unit root tests
Variable
ADF
PP
p
p_ppi
m
w
y
e
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
Estimating the inflation rate
VAR analysis Johansen cointegration testUnrestricted Cointegration Rank TestHypothesizedTrace5 Percent1 PercentNo. of CE(s)EigenvalueStatisticCritical ValueCritical ValueNone ** 0.671155 144.7644 87.31 96.58At most 1 ** 0.516027 90.26820 62.99 70.05At most 2 ** 0.429085 54.70761 42.44 48.45At most 3 * 0.296258 27.24240 25.32 30.45At most 4 0.185047 10.02659 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% levelHypothesizedMax-Eigen5 Percent1 PercentNo. of CE(s)EigenvalueStatisticCritical ValueCritical ValueNone ** 0.671155 54.49624 37.52 42.36At most 1 * 0.516027 35.56059 31.46 36.65At most 2 * 0.429085 27.46521 25.54 30.34At most 3 0.296258 17.21581 18.96 23.65At most 4 0.185047 10.02659 12.25 16.26 *(**) denotes rejection of the hypothesis at the 5%(1%) level Max-eigenvalue test indicates 3 cointegrating equation(s) at the 5% level Max-eigenvalue test indicates 1 cointegrating equation(s) at the 1% levelA. CPI model
impulse response functions variance decomposition Variance Decomposition of P: PeriodS.E.PEMWY 1 0.056720 100.0000 0.000000 0.000000 0.000000 0.000000 2 0.114275 93.85281 2.451476 2.716686 0.892874 0.086158 3 0.158674 87.38295 3.384578 3.737108 4.504839 0.990522 4 0.192170 80.12246 4.351817 3.949469 9.803749 1.772504 5 0.214628 74.43547 5.603106 3.842002 13.63369 2.485737 6 0.230038 70.31289 6.864842 3.870439 15.82085 3.130981 7 0.241042 67.40471 7.922160 4.046781 16.91413 3.712217 8 0.249382 65.35814 8.715221 4.348139 17.40862 4.169876 9 0.256012 63.91147 9.275760 4.725601 17.60252 4.484657 10 0.261499 62.87060 9.657330 5.134743 17.67093 4.666394
SHORT-RUN DYNAMICS VEC representationD(P) = C(1)*( P(-1) - 1.170937515*W(-1) + 0.2982640716*Y(-1) + 0.002647872491*(@TREND(91:1)) + 8.729001573 ) + C(2)*( E(-1) - 1.222579591*W(-1) + 0.03272644293*Y(-1) - 0.008874279592 *(@TREND(91:1)) + 7.60235849 ) + C(3)*( M(-1) - 1.069243511 *W(-1) - 0.7280624046*Y(-1) - 0.006766951579*(@TREND(91:1)) + 0.0527490756 ) + C(4)*D(P(-1)) + C(5)*D(P(-2)) + C(6)*D(P(-3)) + C(7)*D(E(-1)) + C(8)*D(E(-2)) + C(9)*D(E(-3)) + C(10)*D(M(-1)) + C(11)*D(M(-2)) + C(12)*D(M(-3)) + C(13)*D(W(-1)) + C(14)*D(W( -2)) + C(15)*D(W(-3)) + C(16)*D(Y(-1)) + C(17)*D(Y(-2)) + C(18) *D(Y(-3)) + C(19)
R2 0.906Adj. R2 0.848
Coefficient
Std. Error
t-Statistic
Prob.
C(1)
-0.814528
0.218811
-3.722515
0.0008
C(2)
0.270003
0.071490
3.776815
0.0007
C(3)
0.121931
0.106420
1.145753
0.2613
D(p(-1))
0.668933
0.207208
3.228324
0.0031
D(p(-2))
0.507886
0.209492
2.424367
0.0218
D(p(-3))
0.054800
0.170047
0.322264
0.7496
D(e(-1))
0.029440
0.103609
0.284145
0.7783
D(e(-2))
-0.371292
0.094693
-3.921026
0.0005
D(e(-3))
-0.126947
0.109797
-1.156195
0.2570
D(m(-1))
0.171201
0.167383
1.022807
0.3149
D(m(-2))
0.280404
0.130784
2.144025
0.0405
D(m(-3))
0.036422
0.174237
0.209038
0.8359
D(w(-1))
-0.064621
0.201443
-0.320789
0.7507
D(w(-2))
-0.058450
0.170338
-0.343144
0.7340
D(w(-3))
-0.090983
0.128050
-0.710530
0.4831
D(y(-1))
0.401300
0.164282
2.442748
0.0209
D(y(-2))
0.143692
0.182006
0.789487
0.4362
D(y(-3))
-0.128014
0.158577
-0.807269
0.4261
constant
-0.012510
0.044949
-0.278315
0.7827
residual tests
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
1.293750
Probability
0.299182
Obs*R-squared
12.11215
Probability
0.059513
ARCH Test:
F-statistic
0.191267
Probability
0.663956
Obs*R-squared
0.198922
Probability
0.655592
stability test
In-sample fit for the model
B. PPI model VAR analysis Johansen cointegration test
Unrestricted Cointegration Rank Test
Hypothesized
Trace
5 Percent
1 Percent
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Critical Value
None **
0.565996
114.6477
87.31
96.58
At most 1 **
0.524582
72.91269
62.99
70.05
At most 2
0.289215
35.73466
42.44
48.45
At most 3
0.210171
18.66540
25.32
30.45
At most 4
0.128351
6.868447
12.25
16.26
*(**) denotes rejection of the hypothesis at the 5%(1%) level
Trace test indicates 2 cointegrating equation(s) at both 5% and 1% levels
Hypothesized
Max-Eigen
5 Percent
1 Percent
No. of CE(s)
Eigenvalue
Statistic
Critical Value
Critical Value
None *
0.565996
41.73502
37.52
42.36
At most 1 **
0.524582
37.17803
31.46
36.65
At most 2
0.289215
17.06926
25.54
30.34
At most 3
0.210171
11.79696
18.96
23.65
At most 4
0.128351
6.868447
12.25
16.26
*(**) denotes rejection of the hypothesis at the 5%(1%) level
Max-eigenvalue test indicates 2 cointegrating equation(s) at the 5% level
Max-eigenvalue test indicates no cointegration at the 1% level
impulse response functions variance decomposition
Period
S.E.
P_PPI
E
M
W
Y
1
0.069582
100.0000
0.000000
0.000000
0.000000
0.000000
2
0.132047
88.09193
9.452873
1.196678
1.136262
0.122255
3
0.160008
80.59072
11.80334
2.924426
4.138142
0.543368
4
0.173924
73.40588
13.48070
4.025327
8.628089
0.460004
5
0.181116
68.70436
14.89957
4.407568
11.42647
0.562037
6
0.186366
65.53107
16.35004
4.608071
12.80272
0.708098
7
0.191102
63.22293
17.86279
4.758302
13.43549
0.720491
8
0.196033
61.31825
19.29533
4.911243
13.79045
0.684724
9
0.200990
59.66170
20.56622
5.058059
14.04124
0.672784
10
0.205728
58.21028
21.66202
5.196492
14.23669
0.694517
SHORT-RUN DYNAMICS VEC representationD(P_PPI) = C(1)*( P_PPI(-1) - 2.48984122*M(-1) + 1.410028341*W(-1) + 1.007226247*Y(-1) + 0.006980471507*(@TREND(91:1)) + 14.88603259 ) + C(2)*( E(-1) - 2.47182148*M(-1) + 1.338029779 *W(-1) + 1.185997149*Y(-1) + 0.01758795447*(@TREND(91:1)) + 11.13242619 ) + C(3)*D(P_PPI(-1)) + C(4)*D(P_PPI(-2)) + C(5) *D(P_PPI(-3)) + C(6)*D(E(-1)) + C(7)*D(E(-2)) + C(8)*D(E(-3)) + C(9)*D(M(-1)) + C(10)*D(M(-2)) + C(11)*D(M(-3)) + C(12)*D(W(-1)) + C(13)*D(W(-2)) + C(14)*D(W(-3)) + C(15)*D(Y(-1)) + C(16)*D(Y( -2)) + C(17)*D(Y(-3)) + C(18)
R2 0.933Adj. R2 0.895
Coefficient
Std. Error
t-Statistic
Prob.
C(1)
-0.606827
0.109469
-5.543350
0.0000
C(2)
0.551595
0.097025
5.685050
0.0000
D(p_ppi(-1))
0.390650
0.148409
2.632246
0.0133
D(p_ppi(-2))
0.608079
0.173151
3.511839
0.0014
D(p_ppi(-3))
-0.030612
0.079954
-0.382868
0.7045
D(e(-1))
0.132318
0.111346
1.188350
0.2440
D(e(-2))
-0.513438
0.115565
-4.442835
0.0001
D(e(-3))
-0.493779
0.130951
-3.770710
0.0007
D(m(-1))
0.102157
0.136725
0.747170
0.4608
D(m(-2))
-0.188631
0.107790
-1.749980
0.0903
D(m(-3))
0.155841
0.112735
1.382366
0.1771
D(w(-1))
0.285759
0.111475
2.563428
0.0156
D(w(-2))
-0.029445
0.126186
-0.233344
0.8171
D(w(-3))
0.013717
0.101413
0.135256
0.8933
D(y(-1))
-0.239335
0.166358
-1.438673
0.1606
D(y(-2))
-0.067026
0.135804
-0.493547
0.6252
D(y(-3))
-0.370331
0.158653
-2.334221
0.0265
constant
0.066437
0.029255
2.270930
0.0305
residual test
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
1.290639
Probability
0.290960
Obs*R-squared
4.051542
Probability
0.131892
ARCH Test:
F-statistic
0.712090
Probability
0.403214
Obs*R-squared
0.732153
Probability
0.392186
stability test
In-sample fit for the model
Conclusions the empirical results indicate that there is a long-run equilibrium relantionship between prices and e, m, w and y; the findings show that in the long run inflation in Romania is positively related to the nominal exchange rate, money growth and nominal wage growth, while it is negatively related to the output (proxied by the industrial production index); in terms of the magnitude of effects shocks to wages and nominal exchange rate have relatively larger impacts on prices; the empirical findings from the error-correction model showed that inflation adjusts to its equilibrium fairly rapidly; in the short run inflation is determined by its past values, the exchange rate and by the output;
the significant and negative coefficient on the variable of the nominal exchange rate implies that the appreciation of the Romanian leu in the transition period has contributed to reducing inflation; in the CPI model, in the short run, inflation is also determined by the money supply while in the PPI model moeny doesnt affect inflation; in the CPI model, in the short run, wages doesnt influence inflation, while in the PPI model it does.
REFERENCES:
Arratibel Olga, Rodriguez-Palenzuela Diego, Thimann Christian (2002)- Inflation dynamics and dual inflation in accession countries: A new keynesian perspective , ECB WP. Brada Josef, King Arthur E.,. Kutan Ali M. (2000) - Inflation bias and productivity shocks in transitions economies: The case of the Czech Republic, ZEI WP. Bertocco Giancarlo, Fanelli Luca, Paruolo Paolo (2002)- On the determinants of inflation in Italy: evidence of cost-push effects before the European Monetary Union. Enders Walter Applied Econometric Time Series, JohnWiley $ Sons, Inc. Engle R. F.,. Granger C. W. J (1987)- Cointegration and error correction: representation, estimation and testing , Econometrica. Gali J., Gertler M., Lopez-Salido J. D. (2001) - European inflation dynamics, European Economic Review. Gerlach Stefan, Svensson Lars E. O. (2000)- Money and inflation in the euro area: A case for monetary indicators?, NBER WP. Golinelli Roberto, Orsi Renzo (2002) - Modelling inflation in EU accession countries: The case of the Czech Republic, Ezoneplus WP.
Golinelli R., Orsi R. (1998)- Exchange rate, inflation and unemployment in East European economies: the case of Poland and Hungary. Golinelli R., Rovelli R.( 2001) - Painless disinflation? Monetary policy rules in Hungary (1991 - 1999). Hamilton James, D. (1994) Time Series Analysis, Princeton University Press. Hubrich Kirstin (Aug. 2003)- Forecasting euro area inflation: does aggregating forecast accuracy? , ECB WP. Johansen Soren (1991) Estimation and Hypothesis Testing of Contegration Vectors in Gaussian Vector Autoregressive Models, Econometrica, vol. 59, No. 6. Kim Byung-Yeong (2001)- Determinants of inflation in Poland: A structural cointegration approach, Bofit Discussion Papers. Lyziak Tomasz (2003) - Consumer inflation expectations in Poland, ECB WP. Mohanty M. S., Klan Marc - What determines inflation in emerging market economies?, BIS WP;
CPI model
Pairwise Granger Causality Tests
Null Hypothesis:
Obs
F-Statistic
Probability
E does not Granger Cause P
50
5.30421
0.00854
P does not Granger Cause E
0.57743
0.56544
M does not Granger Cause P
50
7.63532
0.00140
P does not Granger Cause M
2.36732
0.10531
W does not Granger Cause P
50
3.24510
0.04825
P does not Granger Cause W
7.33230
0.00175
Y does not Granger Cause P
50
0.06496
0.93720
P does not Granger Cause Y
2.15490
0.12773
M does not Granger Cause E
50
1.44232
0.24710
E does not Granger Cause M
4.00100
0.02516
W does not Granger Cause E
50
1.82747
0.17255
E does not Granger Cause W
6.31042
0.00384
Y does not Granger Cause E
50
1.73196
0.18852
E does not Granger Cause Y
0.42012
0.65952
W does not Granger Cause M
50
4.28134
0.01986
M does not Granger Cause W
0.03292
0.96764
Y does not Granger Cause M
50
0.27489
0.76092
M does not Granger Cause Y
0.41005
0.66607
Y does not Granger Cause W
50
0.99528
0.37761
W does not Granger Cause Y
3.67962
0.03311
PPI model
Null Hypothesis:
Obs
F-Statistic
Probability
E does not Granger Cause P_PPI
50
11.1745
0.00011
P_PPI does not Granger Cause E
0.21038
0.81107
M does not Granger Cause P_PPI
50
8.90000
0.00055
P_PPI does not Granger Cause M
1.69156
0.19574
W does not Granger Cause P_PPI
50
5.61756
0.00664
P_PPI does not Granger Cause W
3.12871
0.05343
Y does not Granger Cause P_PPI
50
0.68804
0.50777
P_PPI does not Granger Cause Y
0.36211
0.69821
M does not Granger Cause E
50
1.44232
0.24710
E does not Granger Cause M
4.00100
0.02516
W does not Granger Cause E
50
1.82747
0.17255
E does not Granger Cause W
6.31042
0.00384
Y does not Granger Cause E
50
1.73196
0.18852
E does not Granger Cause Y
0.42012
0.65952
W does not Granger Cause M
50
4.28134
0.01986
M does not Granger Cause W
0.03292
0.96764
Y does not Granger Cause M
50
0.27489
0.76092
M does not Granger Cause Y
0.41005
0.66607
Y does not Granger Cause W
50
0.99528
0.37761
W does not Granger Cause Y
3.67962
0.03311