We examine real business cycle convergence for 41 euro area regions and 48 US states. Results obtained by a panel model with spatial correlation indicate that the relevance of common business cycle factors is rather stable over the past two decades in the euro area and the US. Ongoing business cycle convergence often detected in a country data is not confirmed at the regional level. The degree of synchronization across the euro area is similar to that to be found for the US states. Thus, the lack of convergence does not seem to be an impediment to a common monetary policy.Business cycle convergence, spatial correlation, spatial panel model

Christian Dreger

Konstantin A. Kholodilin

Michael Artis

English

Research Papers in Economics

Deutsches Institut für 
Wirtschaftsforschung
www.diw.de
Michael Artis ￿ Christian Dreger ￿ Konstantin Kholodilin
Berlin, February 2009
Common and Spatial Drivers 
in Regional Business Cycles
859
Discussion Papers 
 
 
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 Common and spatial drivers in regional business cycles 
 
Michael Artis, Christian Dreger, Konstantin Kholodilin
1 
 
 
Abstract: We examine real business cycle convergence for 41 euro area regions and 48 
US states. Results obtained by a panel model with spatial correlation indicate that the 
relevance of common business cycle factors is rather stable over the past two decades in 
the euro area and the US. Ongoing business cycle convergence often detected in a coun-
try data is not confirmed at the regional level. The degree of synchronization across the 
euro area is similar to that to be found for the US states. Thus, the lack of convergence 
does not seem to be an impediment to a common monetary policy. 
 
JEL: E32, C51, E37 
Keywords: Business cycle convergence, spatial correlation, spatial panel model 
 
                                                 
1 Artis, Economics Department, University of Swansea. Singleton Park, Swansea SA2 8PP email: Mi-
chael.Artis@manchester.ac.uk. Dreger (corresponding author): German Institute for Economic Research 
(DIW Berlin), Germany. Phone: +49-30-89789529, email: cdreger@diw.de. Kholodilin: German Institute 
for Economic Research (DIW Berlin), Germany. Phone: +49-30-89789361, email: kkholodilin@diw.de. 
  11 Introduction 
The degree of comovement of economic activity across states or regions is an issue of 
considerable importance to policy-makers. Asymmetric business cycles are often seen 
as inimical to the formation of a common currency area, although it has been argued 
that a common monetary policy in itself may reduce the cyclical asymmetry (Frankel 
and Rose 1998). 
Economic theory does not provide a conclusive answer regarding the impact of eco-
nomic integration on the synchronization of output fluctuations: see Backus, Kehoe and 
Kydland (1995) and Baxter and Kouparitsas (2005) and the issue remains an empirical 
one. Many authors have investigated the issue, as for example Artis and Zhang (1997), 
Kose, Otrok and Whiteman (2003), Artis, Krolzig and Toro (2004), Stock and Watson 
(2005) and Canova, Ciccarelli and Ortega (2006). Most of them have detected a ten-
dency for national business cycles to converge during the integrative period of the sec-
ond globalization from the 1960s. Artis and Okubo (2008) provide a long-run historical 
perspective which, by revisiting the era of the first globalization before the First World 
War, demonstrates a tendency for globalization to produce a high degree of synchroni-
zation in national business cycles. 
While these findings are based on country data, little work has been done at the regional 
level. There is some indication that European monetary integration has boosted conver-
gence, although the impact of national borders is quite strong (see Montoya and De 
Haan, 2007). While deeper trade integration exerts a positive effect on synchronisation, 
specialisation and exchange rate volatility appear to be the main sources of dispersion 
(Tondl and Traistaru-Siedschlag, 2006). Business cycles differ also across US states, 
see Owyang, Piger, and Wall (2005) as does the response to monetary policy (Carlino 
and De Fina, 1998 and 1999, Fratantoni and Schuh, 2003, Hanson, Hurst and Park, 
2006). There is some evidence that synchronization has decreased since the late 1980ss, 
implying that the US matches the optimum currency area criteria less well than earlier. 
The contradiction between what is commonly seen as being a highly successful mone-
tary policy at the national level and this evidence of increasing asymmetry between re-
gional cycles might be traced to a trade-off between national cycle volatility and re-
gional synchronization (Partridge and Rickman, 2005). 
  2Despite the attendant complications, exploiting a larger information set offers the possi-
bility that exploring the regional dimension will provide new insights. Regions tend to 
be more open to trade than countries and the degree of specialisation is higher than at 
the national level. If diverging trends cancel out in the aggregate, policy conclusions 
based on national evidence can be misleading. In addition, regional comovements may 
be caused not only by common business cycles, but also by non-economic factors repre-
sented in the geographical map pattern; whilst this is linked to industrial structures and 
migration, it can also reflect habits, heritage and culture. Spatial spillovers have been 
largely neglected in previous studies, thereby creating omitted variable bias. A panel 
model allowing for spatial correlation is a convenient way to capture these effects. 
 
2  Panel models with spatial dependencies 
Dependencies along the regional dimension can be approximated by a spatial ARMA 
model 
(1)  y Wy X W         
where y is the endogeneous variable with observations from n regions, X a matrix of k 
explanatory variables and ε the error term (see Anselin, 2001). Spatial spillovers are 
captured by the introduction of spatial lags of the endogeneous variable or by spatial 
correlation in the error term. W denotes a nxn matrix of spatial weights, with elements 
equal to 0 or 1, depending on whether two regions share a common border (1) or not 
(0). Higher spatial lags can be embedded by defining W in a cumulative form (Anselin, 
2001). A row-standardized form of the matrix is often used to extract the mean of ob-
servations from contiguous regions. The Moran statistic 
(2) 
'
'
y Wy
m
y y
  
is an overall measure of the strength of regional dependencies. The linkages may be 
driven by different forces, including business cycle comovements. 
  3The model (1) refers to a pure cross sectional framework. To explore the impact of com-
mon drivers on regional business cycles, the time series dimension has to be added. This 
is done by estimating a panel model allowing for spatial effects: in this we follow the 
instructive leads given by Elhorst (2003) and Baltagi and Li (2006). 
 
3  Data and results 
Data for 41 EU regions and 48 US states are exploited:  see table 1 for the list of re-
gions. Annual data on regional economic activity are available for the 1982-2007 pe-
riod. Some regions are excluded for data reasons. Euro area series refer to GVA at 2000 
prices reported by Cambridge Econometrics. Real GDP data for US states chained in 
2000 dollars are from the BEA. State level data prior to 1991 are reconstructed using 
BEA quantity indexes. The analysis refers to the cyclical component of regional GDP. 
This is defined as a deviation from trend, where the latter is obtained by a HP filter. 
Following Ravn and Uhlig (2002), the smoothing parameter is set equal to 6.25. The 
results do not depend critically on this choice of filter: very similar estimates are ob-
tained when GVA/GDP growth rates are used instead.  
 
-Table 1 and Figure 1 about here- 
 
Figure 1 shows the rolling Moran coefficient, i.e. (2) calculated as an average over a 
moving window of eight years. The dependencies between EU regions were rather weak 
in the 1980s. Perhaps fostered by the European integration process, the correlation rose 
until the mid-1990s. After a decline during the new economy boom, regional spillovers 
regained importance in recent years. The initial correlation was quite high in US re-
gions; after that the dependencies decreased until the middle of the sample period. Sub-
sequently, over more recent years the strength of spillovers again increased gradually. 
 
 
  4The Moran coefficient is an overall measure of regional dependencies. To explore the 
role of supraregional factors in this process, area wide business cycles are extracted as 
the first two principal components of regional cycles, as suggested by the information 
criteria (Bai and Ng, 2002). On average, the factors represent two thirds of cyclical out-
put fluctuations, a share which declines towards the end of the sample. Regression re-
sults for different specifications are shown in table 2. The top panel of that table reports 
results for the whole sample period, the lower two for two successive sub-samples. 
 
-Table 2 about here- 
 
Business cycle comovements are important to explain regional output fluctuations. The 
parameters are robust across subperiods. The inclusion of spatial effects improves the 
model fit. Nevertheless, common business cycles do not account for a larger share of the 
regional economic evolution in more recent years. Therefore, a tendency towards a 
higher cyclical synchronization often found in country panels cannot be confirmed at 
the regional level. 
 
 
 
 
 
 
 
 
 
  5References 
Anselin, L. (2001): Spatial econometrics, in Baltagi, B.H. (ed.): A companion to theo-
retical econometrics, Malden, Blackwell, 310-330. 
Artis, M., Krolzig, H.-M., Toro, J. (2004): The European business cycle, Oxford Eco-
nomic Papers 56, 1-44. 
Artis, M. and Okubo,T. (2008): Globalization and business cycle transmission, CEPR 
Discussion Papers 7041. 
Artis, M., Zhang, W. (1997): International business cycles and the ERM. Is there a 
European business cycle?, International Journal of Finance and Economics 2, 1-16. 
Backus, D.K., Kehoe, P.J., Kydland, F.E. (1995): International business cycles. Theory 
and evidence, in Cooley, T. (ed.): Frontiers of business cycle research, Princeton Uni-
versity Press, Princeton, 331-356. 
Baltagi, B.H., Li, D. (2006): Prediction in the panel data model with spatial correlation: 
the case of liquor, Spatial Economic Analysis 1, 175-185. 
Baxter, M., Kouparitsas, M.A. (2005): Determinants of business cycle comovement: A 
robust analysis, Journal of Monetary Economics 52, 113-157. 
Canova, F., Ciccarelli, M., Ortega, E. (2006): Similarities and convergence in G-7 cy-
cles, Journal of Monetary Economics 54, 850-878. 
Carlino, G., De Fina, R. (1998): The differential regional effects of monetary policy, 
Review of Economics and Statistics 80, 572-587. 
Carlino, G., De Fina, R. (1999): The differential regional effects of monetary policy: 
Evidence from US states, Journal of Regional Science 39, 339-358. 
Elhorst, J.P. (2003): Specification and estimation of spatial panel data models, Interna-
tional Regional Science Review 26, 244-268. 
Elhorst, J.P. (2009): Spatial panel data models, in: Fischer, M., Getis, A. (eds): Hand-
book of Applied Spatial Analysis, Chapter 2. 
Frankel, J.A., Rose A.K. (1998): The endogeneity of the optimum currency area criteria, 
Economic Journal 108, 1009-1025. 
  6Frantantoni, M., Schuh, S. (2003): Monetary policy, housing, and heterogeneous re-
gional markets, Journal ofMoney, Credit and Banking 35, 557–589. 
Hanson, M.S., Hurst, E., Park, K.Y. (2006): Does monetary policy help least those who 
need it most?, Wesleyan University Economic Working Papers 2006-006. 
Kose, M.A., Otrok, C., Whiteman, C.H. (2003): International business cycles. World, 
region and country-specific factors, American Economic Review 93, 1216-1239. 
Montoya, L.A., De Haan, J. (2007): Regional business cycle synchronization in 
Europe?, Bruges European Economic Research Paper 11, International Economics and 
Economic Policy, forthcoming. 
Owyang, M.T., Piger, J., Wall, H.J. (2005): Business cycle phases in US states, Review 
of Economics and Statistics 87, 604-616. 
Partridge, M.D., Rickman, D.S. (2005): Regional cyclical asymmetries in an optimal 
currency area: An analysis using US state data, Oxford Economic Papers 57, 373-397. 
Ravn M, Uhlig H. (2002): On adjusting the HP-filter for the frequency of observations, 
Review of Economics and Statistics 84, 371-375.  
Stock, J.H., Watson, M.W. (2005): Understanding changes in international business 
cycle dynamics, Journal of the European Economic Association 3, 968-1006. 
Tondl, G., Traistaru-Siedschlag, I. (2006): Regional growth cycle synchronisation 
within the euro area, Economic and Social Research Institute, Dublin, Working Paper 
173. 
 
  7Table 1: Euro area and US regions 
A Euro  area  regions 
Belgium: Brussels, Vlaams Gewest, Wallonne. Germany: Baden-Wuerttemberg, Bay-
ern, Bremen, Hamburg, Hessen, Niedersachsen, Nordrhein-Westfalen, Rheinland-Pfalz, 
Saarland, Schleswig-Holstein. Spain: Noroeste, Communidad de Madrid, Centro, Este, 
Sur. France: Île de France, Bassin Parisien, Nord–Pas-de-Calais, Est, Ouest, Sud-Ouest, 
Centre-Est, Méditerranée. Italy: Nord Ovest, Nord Est, Centro, Sud, Isole. Luxembourg: 
Luxembourg. Netherlands: Nord-, Oost-, West-, Zuid-Nederland. Austria: Ost-, Sued-, 
Westoesterreich. Portugal: Continente. 
 
B US  regions 
All states excluding Alaska and Hawaii 
 
  8Table 2: Components of regional output growth 
1982-2007 
 Euro  area  US 
Factor 1  0.131 
(14.37) 
0.177 
(20.15) 
0.118 
(12.72) 
0.188 
(17.24) 
Factor 2  0.035 
(2.67) 
0.047 
(2.70) 
0.010 
(0.76) 
0.016 
(0.769) 
Spatial lag  0.262 
(7.32) 
 0.371 
(11.30) 
 
Spatial error    0.258 
(7.71) 
 0.362 
(11.30) 
R-squared  0.429  0.430 0.415 0.417 
Log  likelihood  3400.27  3400.26 3802.34 3802.93 
 
1982-1994 
 Euro  area  US 
Factor 1  0.128 
(9.73) 
0.174 
(12.98) 
0.107 
(8.62) 
0.194 
(12.00) 
Factor 2  0.033 
(1.94) 
0.044 
(1.94) 
0.020 
(1.20) 
0.036 
(1.26) 
Spatial lag  0.264 
(5.22) 
 0.447 
(10.34) 
 
Spatial error    0.269 
(5.72) 
 0.428 
(10.02) 
R-squared 0.399  0.397  0.486  0.483 
Log likelihood  1578.46  1577.35  1907.90  1905.50 
 
1995-2007 
  9 Euro  area  US 
Factor 1  0.143 
(11.64) 
0.186 
(18.34) 
0.137 
(9.03) 
0.189 
(11.50) 
Factor 2  0.061 
(1.81) 
0.080 
(1.80) 
-0.021 
(0.79) 
-0.028 
(0.791) 
Spatial lag  0.231 
(4.48) 
 0.279 
(5.61) 
 
Spatial error    0.235 
(4.90) 
 0.285 
(5.936) 
R-squared 0.529  0.527  0.342  0.340 
Log likelihood  1939.95  1935.61  1902.72  1900.52 
Note: 41 EU regions, 48 US states. Regional GVA and GDP growth rate explained by first two principal 
components (factor 1, factor 2), spatial lags and spatial errors of first order. Panel regression with fixed 
regional effects, t-values in parantheses. R-squared adjusted. 
 
Figure 1: Rolling Moran coefficient 
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Note: Average Moran coefficient for first order spatial autocorrelation between EU and US (dashed line) 
regions. 
 
  10

Common and Spatial Drivers in Regional Business Cycles

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