Demand and supply shocks synchronization – Evidence from Romania in the context of European Integration

MSc Student: Nora RusuSupervisor: Professor PhD. Moisă Altăr

Academy of Economic Studies Doctoral School of Finance and Banking

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

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

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?

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 EMUFidrmuc 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

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)

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)

Theoretical considerations

The Aggregate Demand and Supply Model (The Model)

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

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

Shock identification

The two variables that compose the VAR:

t

tt p

yX

tptptt XXBX ...110

εt is a vector of the two structural (demand and supply) errors. Assuming that B is invertible, that is )01( 2112 bb

tptptt BXBXBBX 1111

10

1 ...

ttt eLXLAX )(The bivariate moving average representation of VAR:

0 2221

1211

i st

dt

ii

iii

t

t

bbbb

Lpy

(1)

(2)

Shock identification

Using (1) we can say that e1t is the one-step forecast error of Δyt. From the BMA representation in (2) we can further obtain that:

stdtt

stdtt

bbe

bbe

)0(22)0(212

)0(12)0(111or

st

dt

t

t

bbbb

ee

)0(22)0(21

)0(12)0(11

2

1

If the b coefficients were known, it would be possible to recover and from the residuals e1 and e2. We need four additional restrictions. We can use the residuals e1 and e2 to construct the covariance matrix so we would know var(e1), var(e2) and cov(e1,e2) .

Shock identification - Restrictions

Restriction 1: )var()var( )0(12)0(111 stdtt bbe

Knowing that E(εdt, εst) = 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:

2)0(12

2)0(111)var( bbe

Restriction 2:

In the same manner we obtain restriction no 2:

2)0(22

2)0(212 )var( bbe

(3)

(4)

Shock identification - Restrictions

Restriction 3]][[ )0(22)0(21)0(12)0(1121 stdtstdttt bbbbee

Assuming once more that the structural disturbances are not correlated and that they have unit variance we obtain restriction no 3:

)0(22)0(12)0(21)0(1121 bbbbeEe tt (5)Restriction 4For all possible realizations of the sequence, demand shocks will have only temporary effects on the sequence if:

0)()(1 )0(210

12)0(110

22

bkabkakk

(6)

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

Data analysis

Growth PricesCountry Mean Standard

DeviationMean Standard

DeviationEA 0.006001 0.004968 0.004638 0.001430

Germany 0.003855 0.005262 0.002017 0.002060

France 0.005541 0.003789 0.003818 0.003459

Italy 0.003283 0.004395 0.005888 0.001365

Romania 0.010964 0.007787 0.010964 0.007787Hungary 0.009352 0.003578 0.015097 0.012894Poland 0.010266 0.007400 0.009495 0.030153Slovakia 0.012120 0.008560 0.014607 0.022207

Data analysis – Initial correlation

Germany

France

Italy

Romania

Poland

Slovakia

Hungary-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

-0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Correlations of Prices

Corr

elat

ions

of G

row

th

Empirical estimation SVAR

Estimate 8 bivariate SVARrealGDPrealGDP (SA) and GDPdeflatorGDPdeflator (SA)

1) Testing for Unit Root – all variables are I(1) → first differences in realGDP and GDPdeflator

d(realGDP) – real growthd(GDPdeflator) – inflation

2) Optimal number of lags – Sequential LR, Akaike, Schwartz, Hannan-Quinn

Optimal LAG length

Country\Criteria Sequential LR AIC SC HQ Chosen

Euro Area 1 1 1 1 11

Germany 1 1 1 1 11

France 2 2 2 2 22

Italy 3 3 1 1 33

Romania 4 4 5 5 44

Slovakia 3 4 3 4 33

Poland 2 3 1 3 33

Hungary 1 2 1 1 22

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

Impose the STRUCTURAL restriction that the aggregate demand aggregate demand shock does not have a permanent effect on outputshock does not have a permanent effect on output → Structural aggreagate demand and supply shocks

Results 1- BQ restriction and overidentifying restrictionsResponse of Output to Demand Shock

.0014

.0016

.0018

.0020

.0022

.0024

.0026

.0028

.0030

1 2 3 4 5 6 7 8 9 10

Accumulated Response of DL_GDP_EA to StructuralOne S.D. Shock2

.0000

.0004

.0008

.0012

.0016

.0020

1 2 3 4 5 6 7 8 9 10

Accumulated Response of DL_GDP_GE1 to StructuralOne S.D. Shock2

-.0004

.0000

.0004

.0008

.0012

.0016

.0020

1 2 3 4 5 6 7 8 9 10

Accumulated Response of DL_GDP_PL to StructuralOne S.D. Shock2

-.0004

.0000

.0004

.0008

.0012

.0016

.0020

.0024

1 2 3 4 5 6 7 8 9 10

Accumulated Response of D(LOG(GDP_RO)) to StructuralOne S.D. Shock2

.002

.003

.004

.005

.006

1 2 3 4 5 6 7 8 9 10

Accumulated Response of DL_GDP_EA to StructuralOne S.D. Shock1

.003

.004

.005

.006

.007

.008

.009

1 2 3 4 5 6 7 8 9 10

Accumulated Response of DL_GDP_GE1 to StructuralOne S.D. Shock1

.005

.006

.007

.008

.009

.010

.011

.012

.013

1 2 3 4 5 6 7 8 9 10

Accumulated Response of DL_GDP_PL to StructuralOne S.D. Shock1

.002

.004

.006

.008

.010

.012

.014

1 2 3 4 5 6 7 8 9 10

Accumulated Response of D(LOG(GDP_RO)) to StructuralOne S.D. Shock1

Results 1- BQ restriction and overidentifying restrictionsResponse of Output to Supply Shock

Results 1- BQ restriction and overidentifying restrictionsResponse of Prices to Demand Shock

.0000

.0004

.0008

.0012

.0016

.0020

1 2 3 4 5 6 7 8 9 10

Accumulated Response of DL_DEFL_EA to StructuralOne S.D. Shock2

.0020

.0022

.0024

.0026

.0028

.0030

.0032

.0034

.0036

1 2 3 4 5 6 7 8 9 10

Accumulated Response of DL_DEFL_GE1 to StructuralOne S.D. Shock2

.01

.02

.03

.04

.05

.06

.07

1 2 3 4 5 6 7 8 9 10

Accumulated Response of DL_DEFL_PL to StructuralOne S.D. Shock2

.006

.007

.008

.009

.010

.011

.012

.013

.014

1 2 3 4 5 6 7 8 9 10

Accumulated Response of D(LOG(DEFL_RO)) to StructuralOne S.D. Shock2

Results 1- BQ restriction and overidentifying restrictionsResponse of Prices to Supply Shock

-.0020

-.0016

-.0012

-.0008

-.0004

.0000

1 2 3 4 5 6 7 8 9 10

Accumulated Response of DL_DEFL_EA to StructuralOne S.D. Shock1

-.0030

-.0028

-.0026

-.0024

-.0022

-.0020

-.0018

1 2 3 4 5 6 7 8 9 10

Accumulated Response of DL_DEFL_GE1 to StructuralOne S.D. Shock1

-.014

-.012

-.010

-.008

-.006

-.004

-.002

.000

1 2 3 4 5 6 7 8 9 10

Accumulated Response of DL_DEFL_PL to StructuralOne S.D. Shock1

-.06

-.05

-.04

-.03

-.02

-.01

.00

1 2 3 4 5 6 7 8 9 10

Accumulated Response of D(LOG(DEFL_RO)) to StructuralOne S.D. Shock1

Result 2

Demand shock: Temporary and positive effect on output (BQ restriction) Permanent and positive effect on pricesSupply 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.

Response of output to a positive demand shockResponse 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

-.0004

.0000

.0004

.0008

.0012

.0016

.0020

.0024

1 2 3 4 5 6 7 8 9 10

Response of DL_GDP_EA to StructuralOne S.D. Shock2

-.0010

-.0005

.0000

.0005

.0010

.0015

.0020

1 2 3 4 5 6 7 8 9 10

Response of DL_GDP_GE1 to StructuralOne S.D. Shock2

-.0005

.0000

.0005

.0010

.0015

.0020

.0025

.0030

1 2 3 4 5 6 7 8 9 10

Response of D(LOG(GDP_FR)) to StructuralOne S.D. Shock2

-.0012

-.0008

-.0004

.0000

.0004

.0008

.0012

.0016

.0020

.0024

1 2 3 4 5 6 7 8 9 10

Response of D(LOG(GDP_RO)) to StructuralOne S.D. Shock2

Result 4 Correlation of aggregate supply shocks

EA GE FR IT RO PL SK HU

EA 1

Germany 0.71 1

France 0.53 0.36 1

Italy 0.47 0.28 0.25 1

Romania 0.15 0 0.27 0.08 1

Poland 0.19 0.27 0.15 0.14 0.13 1

Slovakia 0.13 0.07 -0.10 0.13 -0.06 -0.05 1

Hungary 0.30 0.12 0.14 0.31 0.17 0.19 -0.30 1

Result 4 Correlation of aggregate demand shocks

EA GE FR IT RO PL SK HU

EA 1

Germany 0.14 1

France 0.02 0.07 1

Italy 0.58 0.34 0.16 1

Romania 0.00 -0.06 -0.14 0.01 1

Poland 0.05 -0.05 0.19 -0.11 -0.01 1

Slovakia 0.08 0.41 0.00 0.28 -0.08 0.25 1

Hungary 0.02 0.00 0.19 -0.04 -0.05 -0.26 -0.39 1

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

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

Germany

FranceItaly

Romania

Poland

Slovakia

Hungary

0.0

0.2

0.4

0.6

0.8

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Correlations of Demand Shocks

Corr

elat

ions

of S

uppl

y Sh

ocks

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:

2004 2006 2008

Supply Shocks

0.54 0.52 0.15

Demand Shocks

-0.37 -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

Result 6 Correlation coefficients of Impulse Response Functions to Supply Shocks

Impulse response of Output Impulse response of Prices

EA Germany France Italy EA Germany France Italy

EA 1 1

Germany 0.99 1 0.88 1

France 0.95 0.92 1 0.72 0.81 1

Italy 0.97 0.96 0.92 1 0.26 -0.16 0.06 1

Romania 0.80 0.85 0.66 0.70 0.26 0.31 0.50 0.33

Poland 0.96 0.92 1.00 0.91 -0.26 -0.34 -0.02 0.47

Slovakia 0.43 0.36 0.63 0.35 -0.50 -0.74 -0.84 0.22

Hungary 0.97 0.96 0.91 0.94 -0.76 -0.88 -0.96 0.02

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

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

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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