Transcript The correlation of Demand and Supply shocks – Evidence

Academy of Economic Studies
Doctoral School of Finance and Banking
New Member States of the European Union on the road to
monetary integration. The case of Romania
MSc Student: Dumitru Daniel
Supervisor: Professor Moisă Altăr
Contents

Motivation and objectives
 Optimal currency area and clustering analysis
– literature review
 Models used and estimation methodology
 Data
 Empirical results
 Conclusions
Motivation



The accession of the NMS to the European Union has stimulated a
growing academic and policy debate about when should the euro
area be extended to the new EU members based on the
achievements in the convergence process.
The current international financial and economic crisis triggered by
the subprime mortgage market from United States led to a rethinking in the euro adoption strategies for some New Member
States (NMS) in order to speed-up the process.
Nevertheless, the euro adoption is still constrained by the fulfillment
of the convergence criteria: nominal convergence and real
convergence.
Nominal convergence (Maastricht convergence
criteria)
Real convergence

A special approach in terms of real convergence assessment is related to
the Optimal Currency Area (OCA) theory, introduced by Mundell (1961).
 From the OCA theory point of view, when a country wants to join in a
monetary union there should be taken into consideration criteria like the
convergence of economic structures, business cycle synchronization,
demand and supply shocks correlation, labor market and market flexibility
in general, level of economic openness etc.
 We study the business cycle synchronization and demand and supply
shocks correlation which are important criteria when we assess the costs of
loosing the monetary policy independence and the exchange rate as an
adjustment mechanism.
Objectives of the paper



investigate business cycles of the New Member States economy
and their symmetry to the Euro Area economy using the correlation
of business cycle approach, based on consensus estimations for
the output gap and Principal Components Analysis (PCA)
identify the demand and supply shocks using the structural vector
autoregression (SVAR) and to look if there is symmetry between
shocks of the New Member States and Euro Area
to use various criteria, namely the Maastricht criteria, in order to
group different countries in clusters, based on a clustering
algorithm
Optimum currency area– literature review
Within OCA theory authors emphasize various criteria:
 Production factors mobility, especially labor force (Mundell, 1961) - can reduce
the need to adjust real factor prices, and the nominal exchange rate, between
countries in response to disturbances.
 The level of economic openness (McKinnon, 1963, Alesina and Barro, 2002).
 Production and consumption diversification (Kenen, 1969, Tavlas, 1993). diminishes the possible impact of shocks specific to any particular sector.
 Wage and price flexibility (Friedman, 1953) - the transition towards adjustment
following a shock is less likely to be associated with sustained unemployment
in one country and/or inflation in another.
 Business cycle synchronization and demand and supply shocks symmetry
(Cohen and Wyplosz, 1989; Weber, 1990; European Commission, 1990) – risks
for an asymmetric shock.
 Fiscal policy integration (Kenen, 1969) and political integration (Mintz, 1970).
 Financial markets integration (Ingram, 1962). Financial market integration can
reduce the need for exchange rate adjustment.
 Inflation differential (Fleming, 1971). Similarities of inflation rates are also
needed to create an OCA.
Optimum currency area and clustering analysis –
literature review

Correlation of business cycles - Boone and Maurel (1998), Fidrmuc (2001),
Darvas and Szapary (2005).

In the field of demand and supply shocks synchronization an important
contribution came from Bayoumi and Eichengreen (1992) when they recovered
the underlying demand and supply disturbances using the technique
developed by Blanchard and Quah (1989).

Fidrmuc and Korhonen (2001), Frenkel and Nickel (2002), Horvath and Ratfai
(2004) conclude that the correlation of shocks varies considerably between
eurozone and accession countries.

For the third objective of our paper Artis and Zhang (1998), Boreiko (2003)
used the both the convergence criteria by applying a clustering algorithm to
find similarities between the countries of European Union.
Models used and estimation methodology
Output gap estimation and the “Consensus” measure
To obtain a measure of business cycles we used the output gap extracted
with the help of four univariate methods: Quadratic trend (QT), HodrickPrescott filter (HP), Band-Pass filter (BP) and Wavelet transformation.
As each filtering techniques has advantages and some weaknesses, we
adopted a method similar to Darvas and Vadas (2005), in order to obtain
a consensus measure of the output gap. A method is considered “better”
if it leads to smaller revisions of past inference as new information
arrived. The method gave weights to the output gaps estimated by the
four filters proportional to the inverse of average revision for each
method.
Principal Components Analysis (PCA) -the output gap is calculated like a
linear combination of the series in the group with weights given by the
first eigenvector of the first component principal .

Consensus

The size of revision at a certain date is:
k t( m )
1

lt
T

s  k 1
_ (m)
(q t , s
_ (m)
1
 q t , s 1 ) 
lt
T

s  k 1
_ (m)
_ (m)
(qt  q t , s )  (qt  q t , s 1 )
The average revision for the method is:
k
( m)
1 T 1 ( m )

kt

T  1 t 1
The weights that will be used are:
Consensus Ouput Gap:
1
(m)
k
m  p
 j 1 1k ( j )
_
p
_
ct   j c
j 1
(m)
t ,T
Supply and demand shocks identification
The model underlying the methodology of recovering shocks is the
Aggregate Supply and Demand model.
Methodology to extract the shocks: Blanchard and Quah (1989) and
Bayoumi and Eichengreen (1992).
Bayoumi and Eichengreen (1992) estimated a VAR with two
variables: differences of GDP and prices level. To identify the
structural shocks they imposed the long run restriction that
demand shocks do not affect the level of output, but have a
permanent impact on prices while supply shocks permanently
affect both output and prices.
Clustering analysis



Kmeans clustering algorithm -used to identify a number of homogenous clusters
in which we could include the twelve countries that entered in European Union
after 2004 ;
The data set consists of n objects (countries) with p variables (various criteria).
Each variable is standardized with zero mean and standard deviation one in order
to treat them as having equal importance in determining the structure. It finds a
partition in which objects within each cluster are as close to each other as
possible, and as far from objects in other clusters as possible.
The dissimilarity coefficient between two objects is defined as a Euclidean
distance:

d (i, j ) 
p
 (x
k 1

ki
 x kj ) 2
The center for each cluster is the point to which the sum of distances from all
objects in that cluster is minimized.
Clustering algorithm

In order to analyze how well the data is partitioned, we used a set of
statistics named the silhouette width for each object and average
silhouette for total dataset.
b(i )  a(i )
s(i ) 
max(a(i ), b(i ))
where a(i) is defined as average dissimilarity of object i to all objects in the
same cluster and b(i) as the minimum across all other clusters of
average dissimilarity of object i to all objects in each cluster.
 When s(i) is close to one it is implied that the object is well classified into
an appropriate cluster. A value near to zero indicates the ambiguity in
deciding to which cluster the object might belong. Negative values
indicate that the object is misclassified.
Data
Sample:
1997Q1-2009Q1, 49 observations
Source of data: Eurostat and NIS
Countries included in the analysis:
 We used eleven countries that entered in EU after 2004 and 2007(Poland,
Czech Republic, Slovakia, Hungary, Slovenia, Estonia, Latvia, Lithuania,
Cyprus, Bulgaria and Romania), nine countries from the eurozone (Belgium,
Germany, Ireland, Spain, France, Italy, Netherlands, Austria, and Portugal),
two EU member countries outside eurozone (Sweden and United Kingdom).
 Variable for the first objective:
-quarterly GDP series (NSA) in constant prices (2000=100) ;
-Tramo/Seats procedure to adjust seasonally ;
Data


For the structural VAR model with two variables, we used real GDP
indices and the GDP deflator. Nominal GDP (NSA) and Real GDP (NSA)
from Eurostat and NIS
Inflation: GDP Deflator = (Nominal GDP) / (Real GDP) * 100
For the structural VAR with three variables, we used the two variables
from above and the real effective exchange rate from Bank of
International Settlements.
 For algorithm of clustering we used the nominal convergence criteria
(without exchange rate criteria because a part of the countries adopted
already euro) for all twelve NMS that entered in EU after 2004.
Empirical results- Results for business cycle
correlation

the output gap based on Quadratic trend, Hodrick Prescott filter and Band
Pass filter using program codes in Eviews 5.1. For the Wavelet
transformation we wrote a procedure in Matlab 7.1.

For the quadratic trend we estimated a regression where the cycle is the
residual of a regression. For Hodrick Prescott filter we used the parameter
equal with 1,600. We used for Band Pass filter the asymmetric Christiano
Fitzgerald approximation with stationarity assumption of I(1) unit root
process ( we tested before the stationarity). We imported the cycles
obtained by Wavelet transformation from Matlab in Eviews.
We started our recursive estimation in 2001Q1. We have 29 estimation of
output gaps for each country, after that we calculate the revisions, the
average revisions, the weights and the consensus.

Revisions of business cycles of Romania
12
ro
QT
8
12
ro
HP
8
6
4
ro
BP
2
4
4
0
0
-2
0
-4
-4
-4
-8
-12
-8
97 98 99 00 01 02 03 04 05 06 07 08
4
3
-6
ro
WT
2
-8
97 98 99 00 01 02 03 04 05 06 07 08
10
8
ro
PCA
6
1
6
ro
Consensus
4
4
0
97 98 99 00 01 02 03 04 05 06 07 08
8
2
2
-1
0
0
-2
-3
-2
-4
-4
-5
-2
-4
-6
97 98 99 00 01 02 03 04 05 06 07 08
-6
97 98 99 00 01 02 03 04 05 06 07 08
97 98 99 00 01 02 03 04 05 06 07 08
Revisions of business cycle of eurozone
4
3
eur
QT
3
2
2
1
1
0
0
-1
-1
-2
-2
-3
-3
-4
-4
HP
eur
WT
1.5
3
BP
1
0
-1
-2
97 98 99 00 01 02 03 04 05 06 07 08
3
eur
2
-5
97 98 99 00 01 02 03 04 05 06 07 08
2.0
eur
eur
PCA
97 98 99 00 01 02 03 04 05 06 07 08
3
2
2
1
1
0
0
-1
-1
eur
Consensus
1.0
0.5
0.0
-0.5
-1.0
-2
97 98 99 00 01 02 03 04 05 06 07 08
-2
97 98 99 00 01 02 03 04 05 06 07 08
97 98 99 00 01 02 03 04 05 06 07 08
Revisions and weights
Revisions
BG
CZ
EE
LV
LT
HU
PL
RO
SI
SK
CY
EUR
NMS avg
QT
0.14
0.18
0.49
0.50
0.41
0.16
0.14
0.30
0.21
0.20
0.09
0.12
0.24
HP
0.11
0.15
0.38
0.41
0.28
0.15
0.09
0.16
0.18
0.20
0.05
0.10
0.19
BP
0.17
0.13
0.35
0.34
0.33
0.15
0.07
0.19
0.23
0.24
0.04
0.12
0.20
WT
0.32
0.18
0.50
0.62
0.34
0.17
0.17
0.21
0.23
0.27
0.13
0.12
0.27
Weights based on principal components
QT
0.34
0.26
0.26
0.26
0.25
0.27
0.27
0.27
0.26
0.26
0.29
0.27
0.27
HP
0.35
0.28
0.28
0.28
0.27
0.29
0.28
0.29
0.26
0.27
0.29
0.27
0.29
BP
0.29
0.24
0.26
0.26
0.25
0.26
0.28
0.22
0.24
0.24
0.29
0.26
0.26
WT
0.01
0.22
0.21
0.20
0.24
0.18
0.16
0.22
0.23
0.23
0.13
0.20
0.19
Weights based on revisions of percentage point output gaps - Consensus
QT
0.29
0.22
0.21
0.22
0.20
0.25
0.19
0.17
0.25
0.28
0.18
0.24
0.23
HP
0.36
0.27
0.28
0.27
0.30
0.27
0.30
0.32
0.30
0.27
0.32
0.29
0.29
BP
0.23
0.29
0.30
0.33
0.25
0.26
0.36
0.26
0.23
0.23
0.37
0.23
0.28
WT
0.12
0.22
0.21
0.18
0.25
0.22
0.15
0.24
0.23
0.21
0.13
0.24
0.20
Correlation of business cycles of the NMS with
eurozone(Consensus)
Bulgaria
Lithuania
Slovakia
Poland
Hungary
Estonia
Latvia
Cyprus
Czech
Republic
Slovenia
0.80
0.60
0.40
0.20
0.00
Romania
Correlation of bussiness cycles of NMS w ith euro area(1997q1-2009q1)
Correlation of business cycles of NMS with euro-area
1997q1-2001q4
2002q1-2009q1
Average 2002q1-2008q2
0.95
2002q1-2008q2
Average 1997q1-2001q4
Average 2002q1-2009q1
0.75
0.55
0.35
Hungary
Romania
Poland
Bulgaria
Lithuania
Latvia
Estonia
Cyprus
Czech
Republic
-0.25
Slovakia
-0.05
Slovenia
0.15
-0.45



We split the sample in two periods to see how the correlation evolved in time.
We found that correlation increased substantially in the last years for all the NMS.
Because we suspect that the recent economic crisis had an impact on the correlation of
business cycles between NMS and eurozone, we computed the correlations excluding the last
3 quarters from the sample .On average the correlation coefficients for 2002q1-2008q2 are
smaller than 2002q1-2009q1
Correlation of business cycles of the eurozone
members with eurozone

We checked also if there is an endogeneity of the OCA, ie. the creation or the joining to
European Monetary Union has impact on business cycles correlation in the sense of
increasing or decreasing it.

Correlation of business cycle of the eurozone members with eurozone:
1.2
1997:1-2001:4
1
2002:1-2009:1
0.8
0.6
0.4
-0.6
Ireland
Portugal
Cyprus
Slovakia
Spain
France
Austria
Belgium
Netherlands
-0.4
Italy
-0.2
Germany
0
Slovenia
0.2
Contemporaneous correlation

The Optimum Currency Area (OCA) theory affirms that the business
cycle correlation should be positive, strong and contemporaneous
between a NMS and eurozone. In order to check the lag for which the
correlation coefficient is the largest, we estimated the correlation of
business cycle with some lags.
 Correlation of business cycle between NMS (at time t+i) and eurozone
(at time t) for 2002q1-2009q1 period:
Correlation of business cycles of the NMS with eurozone

Correlation of business cycle between NMS (at time t+i) and eurozone (at time
t) for 1997q1-2009q1 period

how much the business cycles of a NMS should be correlated with the
business cycle eurozone in order to have net benefits from euro adoption?
Artis (2004) argues that the literature doesn’t help us too much but probably
the best criteria will be that the NMS country which wants to adopt euro
should not have smaller synchronization with eurozone than the existing
members of eurozone does. Fidrmuc and Korhonen (2006) said that if
business cycle correlation in a new EU member state is higher than
correlation of a peripheral euro area economy (e.g. Ireland , Portugal) we have
confidence that the NMS has progressed far enough in fulfilling this OCA
criterion.
Correlation of business cycles of EU members with
eurozone:
1.00
1997q1-2001q4
2002q1-2009q1
0.75
0.50
0.25
0.00
United Kindom
Hungary
Romania
Poland
Bulgaria
Ireland
Lithuania
Portugal
Estonia
Latvia
Cyprus
Sweden
Czech Republic
Slovakia
Spain
France
Austria
Belgium
Slovenia
Netherlands
Italy
-0.50
Germany
-0.25
For the recent period, Slovenia, Slovakia and Czech Republic has
even a higher correlation with eurozone than in the case of the
other old members of EMU.
Results for demand and supply shocks correlation




For the structural VAR decomposition we worked with real GDP growth
series and inflation rate. We used the Akaike criterion to select the optimal
lag length for the VAR.
After we obtained the form of VAR we imposed the structural restriction
suggested by Bayoumi and Eichengreen (1992),
We defined a matrix of restrictions in Eviews with two lines and two
columns that contain the structural condition and that matrix pass on the
effects of shocks to the two variables growth GDP rates and inflation rate.
We made the residuals (shocks) for each country and then we studied the
similarity of shocks by doing the correlation between demand shocks of a
NMS and eurozone and afterwards the correlation between supply shocks.
Correlation of demand (OY axis) and supply shocks (OX axis),
1997-2009
1
Euro zone
0.8
IT
0.6
0.4
DE
NL
ES
FR
0.2
AT IE
PT
LT
CY
0
SK
PL
UK CZ
BG
-0.2
SI
SE
HU
LV
BE RO
EE
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Correlation of demand (OY axis) and supply shocks (OX axis),
2002-2009
1
Euro zone
0.8
IT
DE
0.6
NL
FR
0.4
HU
ES
LV
IE
0.2
LT
SE
PT
SK
PL
0
UK
CY
SI
EE
RO
AT
CZ
-0.2
BE
BG
-0.4
0
0.2
0.4
0.6
0.8
1
Supply (left) and demand (right) shocks for Romania
and eurozone, 2002-2009
.03
.03
.02
.02
.01
.01
.00
.00
-.01
-.01
-.02
-.02
-.03
-.04
-.03
2002
2003
2004
2005
Romania
2006
2007
Eurozone
2008
2002
2003
2004
2005
Romania
2006
2007
Eurozone
2008
Structural VAR model with three variables


GDP growth rate, inflation rate and real exchange rate growth rate.
Three types of shocks exert influence in this specification of VAR:
supply, real demand (IS), and nominal demand shocks.
This is based on an assumption that GDP growth rate (supply) is in the longterm horizon independent of both real exchange rate and inflation rate.
Real exchange rate growth rate (real demand) may in the long-term
horizon depend on GDP growth rate, but it is independent of inflation
rate. Inflation rate (nominal demand) may depend on both GDP growth
rate and real appreciation rate.
Correlation of shocks between Romania and eurozone:
Results for clustering analysis



We used again the Matlab 7.1 package to perform a Kmeans
clustering algorithm to find homogenous groups in data.
Allocation of countries by clusters:
If we look at the value of average silhouette we can say that from
statistical point of view, the data was well partitioned.
Conclusions (I)




This paper assesses the degree of readiness of New Member States (NMS)
of European Union, including Romania, to adopt euro, mainly based on
optimal currency area (OCA) criteria.
Using a consensus measure of output gap computed from 4 filtering
techniques plus a benchmark method based on Principal Component
Analysis (PCA), we estimated the business cycle correlation between NMS
and eurozone. Our findings suggest that the correlation of the business
cycle in the case of Romania is one of the lowest among NMS, followed by
Bulgaria, Slovakia and Lithuania although increased tremendously in the
last years.
Our results suggest also that the financial and economic crisis which hit the
world economy recently led to an increase in the business cycle correlation
between NMS and eurozone, as the countries are simultaneously faced with
a sharp GDP contraction.
According with our results, for the most recent sample (2002-2009), the
business cycle of the NMS countries outside the eurozone is
contemporaneously and positive correlated with eurozone.
Conclusions (II)





For the most of the NMS the correlations of demand shocks with eurozone
are negative, excepting Slovenia and the correlations of supply shocks with
eurozone are positive except Cyprus.
In the case of Romania, our results suggest that the supply shocks are
correlated positively with eurozone and the correlation is quite high.
Nevertheless, in terms of demand shocks, the correlation is negative.
We performed also a structural VAR model with three variables for Romania
and eurozone: GDP growth rate, inflation rate and real exchange rate growth
rate. The results show that the demand shocks for Romania are negatively
correlated with eurozone for the full sample, both for real and nominal
shocks.
Using the Maastricht criteria for the NMS, we identified in the paper some
homogenous groups of countries based on clustering analysis. According
with our estimations, the optimal number of clusters for the NMS should be
4, Romania being in the same cluster with Estonia, Lithuania and Latvia.
The main conclusion of our paper is the fact that Romania, as well as some
other NMS countries still need time to progress on the real convergence
criteria in order to adopt euro without major costs.
References













Afonso, A. and D. Furceri (2007), "Sectoral Business Cycle Synchronization in the European Union",
Department of Economics at the School of Economics and Management (ISEG), Technical University
of Lisbon, Working Papers No. 02.
Alesina, A., R. J. Barro, and S. Tenreyro (2002), "Optimal Currency Areas", National Bureau of
Economic Research Working Papers, 9072
Artis, M. and Zhang, W.(1998), Membership of EMU: A Fuzzy Clustering Analysis of Alternative
Criteria”, EUI Working Paper 98/52.
Artis, M., M. Marcellino and T. Proietti (2004) “Characterising the Business Cycle for the Accession
Countries”, Innocenzo Gasparini Institute for Economic Research (IGIER), working paper no. 261
Bayoumi, T. and B. Eichengreen (1992), “Shocking Aspects of Monetary Unification,” NBER Working
Paper No.3949
Blanchard, O. and D. Quah (1989), “The dynamic effects of aggregated demand and supply
disturbances”, American Economic Review, Vol. 79, No. 4, pp. 655-673
Boone, L. and M. Maurel, (1998), “Economic Convergence of the CEECs with the EU”, Centre for
Economic and Policy Research Discussion Paper, no. 2018, 1998
Boreiko, D. (2002), "EMU and Accession Countries: Fuzzy Cluster Analysis of Membership",
Österreichische Nationalbank Working Paper, No. 71.
Canova, Fabio, 1998. Detrending and Business Cycle Facts. Journal of Monetary Economics 41, 475512.
Cohen, D. and C. Wyplosz (1989), "European Monetary Union: An Agnostic Evaluation", CEPR
Discussion Paper Series, No. 306.
Conway, Paul and D. Frame (2000):” A spectral analysis of New Zealand output gaps using Fourier
and wavelet techniques”, paper presented at Reserve Bank of New Zealand.
Darvas, Z. and G. Szapấry (2004),”Business Cycle Synchronization in the Enlarged EU:
Comovements in the New and Old Members”, Magyar Nemzeti Bank
Darvas, Z. and G. Vadas (2005), ”A New Method for Combining Detrending Techniques with
Application to Business Cycle Syncronization of the New EU Members”, Magyar Nemzeti Bank
References












DeGrauwe, P. (1997), “The Economics of Monetary Integration”, Oxford: Oxford University Press.
Enders, W. (2004), “Applied Econometric Time Series” 2nd ed. Wiley.
European Commission (1990), “One Market, One Money. An Evaluation of the Potential Benefits and Costs of
Forming an Economic and Monetary Union”, European Economy, No. 44.
Fidrmuc, J. and I. Korhonen (2006), ”Meta-Analysis of the Business Cycle Correlation between the Euro Area
and the CEECs”, CESifo Working Paper No. 1693
Fidrmuc, J., Korhonen, I. (2001), “Similarity of Supply and Demand Shocks between the Euro Area and the
CEECs,” BOFIT Discussion Paper 13, Bank of Finland, Institute for Economies in Transition, Helsinki
Fleming, M. J. (1971), "On Exchange Rate Unification", Economic Journal, 81(323): 467-88.
Frankel, J. A., Rose A.K. (1996), “The Endogeneity of the Optimum Currency Area Criteria”, CEPR Discussion
Paper No. 1473
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
Friedman, M. (1953), The Case for Flexible Exchange Rates. Essays in Positive Economics, Chicago:
University of Chicago Press.
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
Ingram, J. C. (1962), Regional Payment Mechanisms: The Case of Puerto Rico, Chapel Hill: University of
North Carolina Press.
Kenen, P. (1969) “The Theory of optimum Currency Areas: An Eclectic View”, in Mundell, R. and A. K.
Swoboda (eds.) Monetary Problems of the International Economy, University of Chicago Press, 41-60
References








McKinnon, R. (1963) “Optimum Currency Areas”, American Economic Review, vol.
52, 717-725
Mintz, N. N. (1970), "Monetary Union and Economic Integration", The Bulletin of
New York University Graduate School of Business Administration (Institute of
Finance), No. 64.
Mongelli, F. P. (2002): “„New“Views on the Optimum Currency Area Theory: What
is EMU Telling Us?” European Central Bank Working Paper, no. 138, April 2002.
Mundell, R. A. (1961), "A Theory of Optimum Currency Areas," American
Economic Review 51.
NBS Research Department (2006), ”The effects of euro adoption on the slovak
economy”, Paper presented at National Bank of Slovakia.
Obstfeld, M. – Krugman, P.R. (1994),” International Economics, Theory and
Policy”. New York, Harper Collins College Publishers 1994.
Rinaldi-Larribe, M.-J. (2008),”Is economic convergence in New Member States
sufficient for an adoption of the Euro?”, The European Journal of Comparative
Economics, Vol. 5, n.2, pp. 133-154.
Weber, A. (1990), "EMU and Asymmetries and Adjustment Problems in the EMS:
Some Empirical Evidence", CEPR Discussion Paper Series, No. 448.