Dissertation paperDeterminants of inflation in Romania

Student: Balan Irina

Supervisor: Professor Moisa Altar

ACADEMY OF ECONOMIC STUDIES

DOCTORAL 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

Figure 1: Anual inflation rate in Romania (%)

0 50 100 150 200 250 300

1990

1992

1994

1996

1998

2000

2002

Romania

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

Variable ADF PP

pp_ppi

mwye

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)

• unit root tests

Estimating the inflation rate

• VAR analysis

• Johansen cointegration test

Unrestricted Cointegration Rank TestHypothesized Trace 5 Percent 1 PercentNo. of CE(s) Eigenvalue Statistic Critical Value Critical 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% level

Hypothesized Max-Eigen 5 Percent 1 PercentNo. of CE(s) Eigenvalue Statistic Critical Value Critical 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% level

A. CPI model

• impulse response functions

• variance decomposition

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of P to P

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of P to E

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of P to M

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of P to W

-.10

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of P to Y

Response to Cholesky One S.D. Innovations ± 2 S.E.

Variance Decomposition of P: Period S.E. P E M W Y 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 representation

D(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 0.906

Adj. R2 0.848

Coefficient Std. Error t-Statistic Prob.

C(1) -0.814528 0.218811 -3.722515 0.0008C(2) 0.270003 0.071490 3.776815 0.0007C(3) 0.121931 0.106420 1.145753 0.2613

D(p(-1)) 0.668933 0.207208 3.228324 0.0031D(p(-2)) 0.507886 0.209492 2.424367 0.0218D(p(-3)) 0.054800 0.170047 0.322264 0.7496D(e(-1)) 0.029440 0.103609 0.284145 0.7783D(e(-2)) -0.371292 0.094693 -3.921026 0.0005D(e(-3)) -0.126947 0.109797 -1.156195 0.2570D(m(-1)) 0.171201 0.167383 1.022807 0.3149D(m(-2)) 0.280404 0.130784 2.144025 0.0405D(m(-3)) 0.036422 0.174237 0.209038 0.8359D(w(-1)) -0.064621 0.201443 -0.320789 0.7507D(w(-2)) -0.058450 0.170338 -0.343144 0.7340D(w(-3)) -0.090983 0.128050 -0.710530 0.4831D(y(-1)) 0.401300 0.164282 2.442748 0.0209D(y(-2)) 0.143692 0.182006 0.789487 0.4362D(y(-3)) -0.128014 0.158577 -0.807269 0.4261constant -0.012510 0.044949 -0.278315 0.7827

• residual tests

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 1.293750 Probability 0.299182Obs*R-squared 12.11215 Probability 0.059513ARCH Test:

F-statistic 0.191267 Probability 0.663956Obs*R-squared 0.198922 Probability 0.655592

• stability test

-16

-12

-8

-4

0

4

8

12

16

1997 1998 1999 2000 2001 2002 2003

CUSUM 5% Significance

• In-sample fit for the model

-.08

-.04

.00

.04

.08

.12

.16

.0

.1

.2

.3

.4

.5

1992 1994 1996 1998 2000 2002

Residual Actual Fitted

B. PPI model

• VAR analysis

• Johansen cointegration test

Unrestricted Cointegration Rank Test

Hypothesized Trace 5 Percent 1 PercentNo. of CE(s) Eigenvalue Statistic Critical Value Critical Value

None ** 0.565996 114.6477 87.31 96.58At most 1 ** 0.524582 72.91269 62.99 70.05At most 2 0.289215 35.73466 42.44 48.45At most 3 0.210171 18.66540 25.32 30.45At 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 PercentNo. of CE(s) Eigenvalue Statistic Critical Value Critical Value

None * 0.565996 41.73502 37.52 42.36At most 1 ** 0.524582 37.17803 31.46 36.65At most 2 0.289215 17.06926 25.54 30.34At most 3 0.210171 11.79696 18.96 23.65At 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

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of P_PPI to P_PPI

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of P_PPI to E

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of P_PPI to M

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of P_PPI to W

-.05

.00

.05

.10

.15

1 2 3 4 5 6 7 8 9 10

Response of P_PPI to Y

Response to Cholesky One S.D. Innovations ± 2 S.E.

• variance decomposition

PeriodS.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 representation

D(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)

Coefficient Std. Error t-Statistic Prob.

C(1) -0.606827 0.109469 -5.543350 0.0000C(2) 0.551595 0.097025 5.685050 0.0000

D(p_ppi(-1)) 0.390650 0.148409 2.632246 0.0133D(p_ppi(-2)) 0.608079 0.173151 3.511839 0.0014D(p_ppi(-3)) -0.030612 0.079954 -0.382868 0.7045

D(e(-1)) 0.132318 0.111346 1.188350 0.2440D(e(-2)) -0.513438 0.115565 -4.442835 0.0001D(e(-3)) -0.493779 0.130951 -3.770710 0.0007D(m(-1)) 0.102157 0.136725 0.747170 0.4608D(m(-2)) -0.188631 0.107790 -1.749980 0.0903D(m(-3)) 0.155841 0.112735 1.382366 0.1771D(w(-1)) 0.285759 0.111475 2.563428 0.0156D(w(-2)) -0.029445 0.126186 -0.233344 0.8171D(w(-3)) 0.013717 0.101413 0.135256 0.8933D(y(-1)) -0.239335 0.166358 -1.438673 0.1606D(y(-2)) -0.067026 0.135804 -0.493547 0.6252D(y(-3)) -0.370331 0.158653 -2.334221 0.0265

constant 0.066437 0.029255 2.270930 0.0305

R2 0.933

Adj. R2 0.895

• residual test

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 1.290639 Probability 0.290960Obs*R-squared 4.051542 Probability 0.131892ARCH Test:

F-statistic 0.712090 Probability 0.403214Obs*R-squared 0.732153 Probability 0.392186

• stability test

-16

-12

-8

-4

0

4

8

12

16

1997 1998 1999 2000 2001 2002 2003

CUSUM 5% Significance

• In-sample fit for the model

-.08

-.04

.00

.04

.08

.12

.0

.1

.2

.3

.4

.5

.6

1992 1994 1996 1998 2000 2002

Residual Actual Fitted

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 doesn’t affect inflation;

• in the CPI model, in the short run, wages doesn’t 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;

Pairwise Granger Causality TestsNull 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

CPI 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

PPI model