1. Demand and supply shocks synchronization – Evidence from Romania in the context of European Integration MSc Student: Nora Rusu Supervisor: Professor PhD. Moisă Altăr Academy of Economic Studies Doctoral School of Finance and Banking

2. Topics of the paper  Optimal currency area and business cycle correlation approach  Brief literature review  Objectives of the paper  Theoretical considerations and shock identification  Data analysis  Empirical estimation (Structural VAR)  Results  Conclusions

3. The importance of studying business cycle correlation in the context of optimal currency area theory  Optimal currency area theory (since 1961 Mundell)  “If nations are dissimilar to regions, fixed exchange rates may do as well as flexible exchange rates”  Formation of the European Union  Is at least the “old” Europe an optimum currency area?  Will it be costly for the economies to adopt a single currency  Formation of the Eurozone  Can it receive new members?  How can one determine if a country is ready or not for adoption  Business cycle correlation → Shock correlation (one approach)  Benefits: reduction in transactions costs and stronger integration of markets

4. The importance of studying business cycle correlation in the context of optimal currency area theory  Costs:  Giving up the flexible exchange rate  Giving up the independence of monetary policy If asymmetric shocks occur If responses to shocks are different  Sole instrument: fiscal policy (limited by Maastricht criteria)  CEECs once they join EU they have to join EMU  Are they ready for EMU adherence?  Is Romania ready for EMU adherence?

5. Brief literature review – Optimal currency area theory Mundell (1961) - the argument for flexible exchange rates rests on the closeness with which countries correspond to regions. If a nation is an economic region with internal factor mobility and external factor immobility, the argument for flexible exchange rates holds. Bayoumi and Eichengreen (1992) when they used data from 11 European Union member countries to extract information on underlying aggregate supply and demand disturbances using VAR decomposition CEECs: - topical subject; they are expected to join EMU Fidrmuc and Korhonen (2001), Horvath (2000), Frenkel and Nickel (2002), Babetski, Boone and Maurel (2003), Horvath and Ratfai (2004), Fridmuc (2001), Frankel and Rose (1998)  The correlation in shocks has a high degree of dispersion and differ from correlations in EMU; still some strong correlations are shown by some countries (Hungary

6. Objectives of the paper  Identify aggregate supply and demand shocks for the countries included in the study  Study the response of the economy (real GDP and GDP deflator) to a supply or demand shocks  Study the correlations between responses  Study the correlations in shocks between the considered countries  Time varying correlations  Shock importance (Error forecast variance decomposition)

7. Shock identification Methodology: Blanchard and Quah (1989), Bayoumi (1991) and Bayoumi and Eichengreen (1992) Blanchard and Quah (1989) They interpret fluctuations in real GNP and unemployment as due to two types of disturbances: disturbances that have a permanent effect on output and disturbances that do not. The first is interpreted as supply disturbances and the second as demand disturbance. Bayoumi and Eichengreen (1991) They examine time series behavior of real GDP and the price level. To identify the structural shocks they impose the restriction that aggregate demand disturbances have only a temporary effect on output but a permanent impact on prices while aggregate supply disturbances permanently affect both output and prices.  Bivariate SVAR (real GDP growth and variation in prices)

8. Theoretical considerations The Aggregate Demand and Supply Model (The Model)

9. Theoretical considerations AD → AD’ + SRAS Equilibrium E → D’ Temporary increase in Output (Y’) Increase in Prices (P’) Supply curve becomes vertical LRAS Equilibrium D’ → D’’ Output returns to its initial level (Y) Permanent increase in Prices (P’’) Positive demand shock:  Temporary positive effect on Output; Long run zero effect  Permanent positive effect on Prices

10. Theoretical considerations Technology shock raises long run potential level of output → both SRAS and LRAS move rightwards to SRAS’ and LRAS’ Short-run equilibrium S’ Increase in Output (Y’) Decrease in Prices (P’) Supply curve becomes vertical LRAS’ Equilibrium S’ → S’’ Output increases further (Y”) Prices decline further (P’’) Positive supply shock:  Permanent positive effect on Output  Permanent decline in Prices

11. Shock identification The two variables that compose the VAR: ε t is a vector of the two structural (demand and supply) errors. Assuming that B is invertible, that is The bivariate moving average representation of VAR: (1) (2)

12. Shock identification Using (1) we can say that e 1t is the one-step forecast error of Δy t. From the BMA representation in (2) we can further obtain that: or If the b coefficients were known, it would be possible to recover and from the residuals e 1 and e 2. We need four additional restrictions. We can use the residuals e 1 and e 2 to construct the covariance matrix so we would know var(e 1 ), var(e 2 ) and cov(e 1,e 2 ).

13. Shock identification - Restrictions Restriction 1: Knowing that E(ε dt, ε st ) = 0 since the two disturbances are uncorrelated and assuming at the same time that the two disturbances have unit variance, we obtain restriction no 1: Restriction 2: In the same manner we obtain restriction no 2: (3) (4)

14. Shock identification - Restrictions Restriction 3 Assuming once more that the structural disturbances are not correlated and that they have unit variance we obtain restriction no 3: (5) Restriction 4 For all possible realizations of the sequence, demand shocks will have only temporary effects on the sequence if: (6)

15. Data analysis Countries included in the analysis: Romania Core economies of the Euro Area: Germany, France and Italy Slovakia Poland Hungary Variables: Nominal GDP (SA and NSA) and Real GDP (Eurostat, IFS (IMF)) Inflation: GDP Deflator = (Nominal GDP) / (Real GDP) * 100 Sample: 1998Q1 : 2008Q1 Initial data managing: Eliminating the seasonal effects using Demetra (TRAMO SEATS) – alternative: seasonal dummies – lost in degrees of freedom

16. Data analysis GrowthPrices CountryMeanStandard Deviation MeanStandard Deviation EA0.0060010.0049680.0046380.001430 Germany0.0038550.0052620.0020170.002060 France0.0055410.0037890.0038180.003459 Italy0.0032830.0043950.0058880.001365 Romania0.0109640.0077870.0109640.007787 Hungary0.0093520.0035780.0150970.012894 Poland0.0102660.0074000.0094950.030153 Slovakia0.0121200.0085600.0146070.022207

17. Data analysis – Initial correlation

18. Empirical estimation SVAR Estimate 8 bivariate SVAR realGDPGDPdeflator realGDP (SA) and GDPdeflator (SA) 1) Testing for Unit Root – all variables are I(1) → first differences in realGDP and GDPdeflator d(realGDP) – real growth d(GDPdeflator) – inflation 2) Optimal number of lags – Sequential LR, Akaike, Schwartz, Hannan-Quinn

19. Optimal LAG length Country\CriteriaSequential LRAICSCHQChosen Euro Area11111 Germany11111 France22222 Italy33113 Romania44554 Slovakia34343 Poland23133 Hungary12112

20. Empirical estimation SVAR 3) VAR stability condition – the absolute values of the eigenvalues of the matrix lie inside the unit circle 4) Residual tests: Autocorrelation (LM Autocorrelation test) Normality (Jarque-Berra test) White Heteroskedasticity test 5) Granger Causality test aggregate demand shock does not have a permanent effect on output Impose the STRUCTURAL restriction that the aggregate demand shock does not have a permanent effect on output → Structural aggreagate demand and supply shocks

21. Results 1 - BQ restriction and overidentifying restrictions Response of Output to Demand Shock

22. Results 1 - BQ restriction and overidentifying restrictions Response of Output to Supply Shock

23. Results 1 - BQ restriction and overidentifying restrictions Response of Prices to Demand Shock

24. Results 1 - BQ restriction and overidentifying restrictions Response of Prices to Supply Shock

25. Result 2 Demand shock:  Temporary and positive effect on output (BQ restriction)  Permanent and positive effect on prices Supply shock:  Permanent and positive effect on output  Permanent and negative effect on prices In most of the cases (as in Frenkel, Nickel and Schmidt (1999)):  The supply shocks seem to be more important then the demand shocks for output response even in the short run Size: The response of output to a supply shock in EA is almost half of the magnitude of the similar reaction at the same type of shock for the Romanian economy.

26. Response of output to a positive demand shock Result 3 Speed of adjustment:  Demand shock to output: very quick absorption in EA (1-3 quarters)  Demand shock to prices stabilizes in 4-5 quarters (EA)  Supply shock to output: 7-8 quarters in EA, 5-6 in CEECs  Supply shock to prices stabilizes quicker in only 3-4 quarters (EA) In general: in CEEC’s it takes longer to absorb the shocks and the effect is volatile

27. Result 4 Correlation of aggregate supply shocks EAGEFRITROPLSKHU EA 1 Germany 0.711 France 0.530.361 Italy 0.470.280.251 Romania 0.1500.270.081 Poland 0.190.270.150.140.131 Slovakia 0.130.07-0.100.13-0.06-0.051 Hungary 0.300.120.140.310.170.19-0.30 1

28. Result 4 Correlation of aggregate demand shocks EAGEFRITROPLSKHU EA 1 Germany 0.141 France 0.020.071 Italy 0.580.340.161 Romania 0.00-0.06-0.140.011 Poland 0.05-0.050.19-0.11-0.011 Slovakia 0.080.410.000.28-0.080.251 Hungary 0.020.000.19-0.04-0.05-0.26-0.39 1

29. Result 4 Correlation of shocks The Euro Area countries  “EMU will reduce the incidence of country specific shocks” (European Commission 1990)  Strong correlation of supply shocks; Weak correlation in demand shocks → not a homogenous zone For CEECs countries:  The correlation of demand shocks is much weaker and confusing in terms if signs than the correlation in supply shocks (Firmuc and Korhonen (2003))  Differences in demand shocks mostly emanate from different economic policies (e.g. Fiscal policy in developing countries) and differences and changes in exchange rate regimes

30. Result 4 The correlation of supply shocks is more important for assessing the degree of business cycle integration

31. Result 5 Correlation in shocks between EA and Romania  Contemporaneous correlation: positive supply shock correlation (0.15) and null demand shock correlation  Time evolution of correlation in shocks: 200420062008 Supply Shocks 0.540.520.15 Demand Shocks -0.37 0.00  Supply shocks correlation positive and rather strong (Caution – small sample reduced the significance (2/√n))  Supply shock correlation decline in 2007 (floods) – different reaction  Demand shock correlation negative: different policies, change in exchange rate regime, complete liberalization of the capital account in 2005

32. Result 6 Correlation coefficients of Impulse Response Functions to Supply Shocks Impulse response of OutputImpulse response of Prices EAGermanyFranceItalyEAGermanyFranceItaly EA 1 1 Germany 0.991 0.881 France 0.950.921 0.720.811 Italy 0.970.960.9210.26-0.160.061 Romania 0.800.850.660.700.260.310.500.33 Poland 0.960.921.000.91-0.26-0.34-0.020.47 Slovakia 0.430.360.630.35-0.50-0.74-0.840.22 Hungary 0.970.960.910.94-0.76-0.88-0.960.02

33. Result 6 Correlation coefficients of Impulse Response Functions to Supply Shocks  Strong correlation in response of the economy to shocks for EA countries; Weaker results for correlation of IRF to Demand Shocks  Lower speed of adjustment in case of some of the CEECs countries: Romania and Slovakia  Strong correlation in response of output to a supply shock for Poland and Hungary

34. Result 7 Forecast error Variance Decomposition  Variation in real GDP growth is explained (after one quarter):  80-95% by supply shocks in EA countries Germany, Italy and France (curiously enough in EA as a whole the percentage is 50%)  70% in Romania  Technology shocks not only dominate variations of real GDP growth in the long run but they are also important for short-term output movements  Variation in GDP deflator inflation is explained almost equally by the two shocks both in EA countries and in CEECs

35. Conclusions  The most important issue: Correlation of supply shocks  Strong and significant correlations between EA countries; still, the correlation among EA countries is not perfect  Significantly weaker shock correlation with CEECs; weaker demand correlation (different policies)  Correlated responses but still differences with CEECs  For Romania, the correlation in supply shocks has been positive and the correlation of demand shocks negative (different policies and exchange rate regimes)  Acceptance of new countries would not affect that much the EA countries. It’s rather a problems of the acceding economies to be correlated with the EA and not to have too high costs.

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37. Selective Bibliography 2 Frenkel, M. and Nickel, C. (2002), “How symmetric are the shocks and the shock adjustment dynamics between the Euro Area and Central Eastern European Countries?”, International Monetary Fund, Working Paper, No. 222 Frenkel, M., C. Nickel and G. Schmidt (1999), “Some shocking aspects of EMU enlargement”, Research note No. 99-4, Deutsche Bank, Frankfurt am Main Horvath, J. (2000), “Supply and Demand Shocks in Europe: Large-4 EU Members, Visegrad-5 and Baltic-3 Countries”, Central European University, mimeo Horvath, J. (2003), “Optimum Currency Area Theory: A Selective Review”, Bank of Finland, Institute for Economies in Transition Discussion Paper Supply and Demand Shocks in Europe: Large-4 EU Members, Visegrad-5 and Baltic-3 Countries”, Central European University, mimeo Horvath, J. and A. Ratfai (2004), "Supply and demand shocks in accession countries to the European Monetary Union," Journal of Comparative Economics, Vol 32, No.2, 202-211 Korhonen, I. (2001), “Some empirical tests on the integration of economic activity between the Euro area and the accession countries”, BOFIT Discussion Paper 9, Bank of Finland, Institute for Economies in Transition, Helsinki Kouparitsas, M., (1999), "Is the EMU a viable common currency area? A VAR analysis of regional business cycles" Economic Perspectives, Federal Reserve Bank of Chicago, issue Q IV, pages 2-20 Mongelli F., (2002) “’New’ views of the optimum currency area theory: What is EMU telling us?”, European Central Bank, Working Paper No. 138 Mundell, R. A. (1961), "A Theory of Optimum Currency Areas," American Economic Review 51