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Spatial propagation of macroeconomic shocks in Europe

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Abstract

This paper implements a spatial vector autoregressive model that takes into account both the time and the spatial dimensions of economic shocks. We apply this framework to analyze the propagation through space and time of macroeconomic (inflation, output gap and interest rate) shocks in Europe. The empirical analysis identifies an economically and statistically significant spatial component in the transmission of macroeconomic shocks in Europe.

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Notes

  1. Note that regional interdependency issues can also be addressed in factor models. Applications include Kose et al. (2003), Forni and Reichlin (1998) and Houssa (2008).

  2. See Anselin (1988), Anselin et al. (2008), Cliff and Ord (1981), Lee and Yu (2010b) and LeSage and Pace (2009).

  3. Demeaned series follow automatically after removing the country- and series-specific fixed effects.

  4. Parent and LeSage (2011) introduce a space-time filter in a dynamic spatial panel model. They show that the coefficient on the temporal lag-spatial component can be restricted to the product of \(\Upphi_{ll}^{i}\rho_{ll}.\) This restriction remains, however, an empirical question. Note that this issue goes beyond the scope of our analysis as we allow for country-specific temporal coefficients.

  5. Similar models have been recently analyzed in the literature (see, for example, Azomahou et al. 2009; Beenstock and Felsenstein 2007; Brady 2009). Moreover, instead of modelling directly the space and time dimensions it is possible to use information on the space dimension in the formulation of priors for Bayesian VAR models (see for instance Krivelyova and LeSage 1999).

  6. Note that the inclusion of cross-variable spatial terms in our SAR model defines a Spatial Durbin Model (see Anselin 1988).

  7. This specification assumes as in Eq. 1 that the United States do not respond to European state variables.

  8. In fact, the Gauss–Markov assumption that explanatory variables are independent from disturbance is violated.

  9. GIRFs and OIRFs are identical for the first shock or if \(\Upsigma\) is diagonal, see Pesaran and Shin (1998).

  10. It is customary to set λ to 1,600 for quarterly data.

  11. From 1999:1, the interest rate series converge for the country members of the European Monetary Union.

  12. For details on this see LeSage (1998) and LeSage and Pace (2009).

  13. We consider this average to account for asymmetries in the reporting of export and import data.

  14. Results are available upon requests.

  15. Note that the spatial coefficients remain highly statistically significant in specifications where allow for a higher order temporal lag.

  16. Temporal lag coefficients are not reported for space considerations but they can be obtained upon request.

  17. The weighting matrix based on sharing borders also provides the highest log-likelihood value for specifications that use higher temporal lags although the result is less robust for interest rates; see Table 1.

  18. The results on shocks related to other countries are available upon request.

  19. The results on the trade based matrix are available upon request.

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Acknowledgments

We would like to thank the editor and two anonymous referees for their comments and suggestions which helped to improve an earlier draft of the paper. We acknowledge financial support from FWO grant No. G.0626.07.

Author information

Authors and Affiliations

  1. National Bank of Belgium (NBB), de Berlaimontlaan 14, 1000, Brussels, Belgium

    Hans Dewachter

  2. CES, University of Leuven, Leuven, Belgium

    Hans Dewachter, Romain Houssa & Priscilla Toffano

  3. CESifo, Munich, Germany

    Hans Dewachter

  4. CRED and CeReFiM-University of Namur, Namur, Belgium

    Romain Houssa

Authors
  1. Hans Dewachter
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  2. Romain Houssa
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  3. Priscilla Toffano
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Correspondence to Hans Dewachter.

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The views expressed in this paper are those of the authors and do not necessarily reflect the ideas of the National Bank of Belgium.

Appendix

Appendix

See Tables 4, 5, 6 and Figs. 1, 2, 3, 4, 5, 6.

Table 4 Europe exposure to the United States, sharing borders
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Table 5 Europe exposure to the United States, physical distances
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Table 6 Europe Exposure to the United States, Trade
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Fig. 1
figure 1

Spatial propagation of German Inflation Shock: GIRFs (Inflation Responses). The figure shows point estimates GIRFs (bold lines) together with the 68% (light shading) and 90% (dark shading) error bounds of inflation in the eleven European countries to a one standard deviation of German inflation shock, where we use a weighting matrix based on sharing borders and one lag for the autoregressive process. We use 500 bootstrapping draws to construct the error bounds. The unit of the GIRFs is in percentage of the standard deviation of the German inflation shock

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Fig. 2
figure 2

Spatial propagation of German Output Shock: GIRFs (Output Responses). The figure shows point estimates GIRFs (bold lines) together with the 68% (light shading) and 90% (dark shading) error bounds of output gap in the eleven European countries to a one standard deviation of German output gap shock, where we use a weighting matrix based on sharing borders and one lag for the autoregressive process. We use 500 bootstrapping draws to construct the error bounds. The unit of the GIRFs is in percentage of the standard deviation of the German output gap shock

Full size image
Fig. 3
figure 3

Spatial propagation of German Interest Rate Shock: GIRFs (Interest Rate Responses). The figure shows point estimates GIRFs (bold lines) together with the 68% (light shading) and 90% (dark shading) error bounds of interest rates in the eleven European countries to a one standard deviation of German interest rate shocks, where we use a weighting matrix based on sharing borders. We use 500 bootstrapping draws to construct the error bounds. The unit of the IRFs is in percentage of the standard deviation of the German interest rate shocks

Full size image
Fig. 4
figure 4

Spatial propagation of German Inflation Shock: OIRFs (Inflation Responses). The figure shows point estimates OIRFs (bold lines) together with the 68% (light shading) and 90% (dark shading) error bounds of inflation in the eleven European countries to a one standard deviation of German inflation shock, where we use a weighting matrix based on sharing borders. We use 500 bootstrapping draws to construct the error bounds. The unit of the OIRFs is in percentage of the standard deviation of the German inflation shock

Full size image
Fig. 5
figure 5

Spatial propagation of German Output Shock: OIRFs (Output Responses). The figure shows point estimates OIRFs (bold lines) together with the 68% (light shading) and 90% (dark shading) error bounds of output gap in the eleven European countries to a one standard deviation of German output gap shock, where we use a weighting matrix based on sharing borders. We use 500 bootstrapping draws to construct the error bounds. The unit of the IRFs is in percentage of the standard deviation of the German output gap shock

Full size image
Fig. 6
figure 6

Spatial propagation of German Interest Rate Shock: OIRFs (Interest Rate Responses). The figure shows point estimates OIRFs (bold lines) together with the 68% (light shading) and 90% (dark shading) error bounds of interest rates in the eleven European countries to a one standard deviation of German interest rate shocks, where we use a weighting matrix based on sharing borders. We use 500 bootstrapping draws to construct the error bounds. The unit of the OIRFs is in percentage of the standard deviation of the German interest rate shocks

Full size image

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Dewachter, H., Houssa, R. & Toffano, P. Spatial propagation of macroeconomic shocks in Europe. Rev World Econ 148, 377–402 (2012). https://doi.org/10.1007/s10290-012-0118-1

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