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Domestic versus international determinants of European business cycles: a GVAR approach

Abstract

We investigate the sources of macroeconomic (output and inflation) variability in selected European countries within and outside the European Monetary Union: Germany, Italy, Austria, the UK, and Poland. We estimate a global vector autoregressive model with quarterly data for fifteen countries and regions covering more than 90 per cent of the World GDP. We find that domestic factors explain most of the macroeconomic variability over the short horizon, i.e. from zero to four quarters, but become progressively dominated by international ones at larger horizons. Regional factors appear to be particularly important. Focusing on the European Monetary Union, we detect no significant differences between countries current members and non-members in the sources of output variability. As for inflation, on the contrary, regional factors are more influential than those of the rest of the world for the EMU member countries, differently from non-members.

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Notes

  1. In this paper, the terms “business cycle”, “macroeconomic fluctuations”, and “macroeconomic variability” are interchangeable.

  2. Austria and Poland may seem more different from one another than the countries in the other groups. For instance, there are about 38 million people in Poland, and no more than 9 million in Austria. However, both can be labelled as small open economies with Germany as their main trading partner (the reason for that could lie in the similar economic structure of the two economies, with agriculture contributing to less than 6 per cent of GDP, industry being around 30 per cent, and services accounting for no more than 70 per cent of GDP in both countries). Moreover, aggregate GDP is not too dissimilar (the Polish one amounted to about 800 billion dollars in 2012, the Austrian one was roughly half of that figure in the same period), and the two countries share a similar geographical location. There are other examples in the literature drawing parallels between these two economies (see, e.g. Havlik 2002).

  3. See Dees et al. (2007) for a different counterfactual exercise using the GVAR methodology.

  4. See Boschi and Girardi (2011) for a similar exercise on Latin American countries.

  5. It is worth noting that the distinction between the international and the exogenous variables at the single VEC model level is not crucial, as both types of variables are treated as weakly exogenous.

  6. A different strand of literature studies the business cycle fluctuations concentrating on either productivity shocks (Smets and Wouters 2007) or demand shocks (Canova and Nicoló 2003).

  7. The choice of the sample period aims at excluding the extraordinary effects of the ongoing financial and fiscal crisis. Mizrach (2008) finds that the ABX index, a benchmark of the performance of a variety of credit default swaps on asset backed securities, exhibits significant jumps as early as mid-2006, well before any problems in the mortgage market were discussed in the press.

  8. The series referring to the developing countries proved to be particularly problematic, so that the choice of the period under investigation is also dictated by the need to eliminate outliers.

  9. Notice that the power of the tests is low, particularly so because of the (relatively) small number of observations for the economies under observation. Therefore, in addition to the information criteria and the results of the tests, both the lag lengths and the cointegrating orders of the models have been chosen considering the following: (a) the best match between the theoretical correlations among residuals derived from the GVAR model and the unconditional correlations computed on the raw series; (b) the consistency between the impulse responses of the country-/region-specific VEC models and those of the GVAR model.

  10. See Boschi (2012) for an example of a long-run structural GVAR model.

  11. Notice that the percentages of the \(k\)-step ahead GFEVD do not sum to 100 due to nonzero covariance between the shocks (Pesaran et al. 2007; Mauro and Pesaran 2013); therefore, entries have been normalized, i.e. divided by their sum, so that they sum to 100.

  12. The dataset used in the analysis is available for download on the personal pages of the first and third author. Detailed explanatory notes accompany the dataset.

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Authors and Affiliations

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Correspondence to Melisso Boschi.

Additional information

We are grateful to Alessandro Girardi, Niall McInerney and Stephen O’Neill for helpful comments. The views expressed in this paper do not necessarily reflect those of the Senate of the Republic of Italy.

Appendix

Appendix

List of countries

Countries included in the eight geographical areas:

  • Western Europe: Belgium, Denmark, Spain, Finland, France, Greece, Ireland, Luxembourg, the Netherlands, Norway, Portugal, Sweden, Switzerland;

  • Europe 2005: Czech Republic, Estonia, Hungary, Lithuania, Lettonia, Slovenia, Slovakia;

  • other European: Albania, Bulgaria, Croatia, Romania, Russia, Turkey, Ukraine;

  • Asia: Hong Kong, Indonesia, India, South Korea, Malaysia, Philippines, Singapore, Thailand, Taiwan;

  • NAFTA: Canada, Mexico;

  • Oceania and South Africa: Australia, New Zealand, South Africa;

  • rest of Africa and Middle East: United Arab Emirates, Algeria, Egypt, Iran, Israel, Libano, Libya, Morocco, Saudi Arabia, Tunisia;

  • Latin America: Argentina, Brazil, Colombia, Chile, Peru, Venezuela.

Data construction

All data have been gathered at the country level from a range of different sources: national central banks, national institutes of statistics, IMF, OECD, and the World Bank.Footnote 12 Datastream has been extremely valuable to obtain many of the series coming from the various sources, and the online databases of the international organizations mentioned above have constituted important sources to reduce the number of missing observations. Reliance on the specific websites of the national institutions mentioned above has been more limited. These data have been integrated in order to obtain the maximum coverage possible for the six variables used in the analysis: GDP, the money stock, the exchange rate, the short-term interest rate, the CPI, and the stock market index.

Data have been deseasonalized when needed, and 2000 has been chosen as the base year for the real terms. GDP and the money stock are all expressed in millions of national currency units. Foreign variables have been calculated for each country using the weighted average of the partner areas/countries using the bilateral trade flows as weights.

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Boschi, M., Marzo, M. & Salotti, S. Domestic versus international determinants of European business cycles: a GVAR approach. Empir Econ 49, 403–421 (2015). https://doi.org/10.1007/s00181-014-0875-x

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  • DOI: https://doi.org/10.1007/s00181-014-0875-x

Keywords

  • Business cycle
  • Inflation
  • European Monetary Union
  • Global VAR (GVAR)

JEL Classification

  • C32
  • E32