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Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions

  • Matías Mayor


    (Department of Applied Economics, University of Oviedo, Spain)

  • Roberto Patuelli


    (Department of Economics, Faculty of Economics-Rimini, University of Bologna, Italy; The Rimini Centre for Economic Analysis (RCEA), Italy)

In any economic analysis, regions or municipalities should not be regarded as isolated spatial units, but rather as highly interrelated small open economies. These spatial interrelations must be considered also when the aim is to forecast economic variables. For example, policy makers need accurate forecasts of the unemployment evolution in order to design short- or long-run local welfare policies. These predictions should then consider the spatial interrelations and dynamics of regional unemployment. In addition, a number of papers have demonstrated the improvement in the reliability of long-run forecasts when spatial dependence is accounted for. We estimate a heterogeneouscoefficients dynamic panel model employing a spatial filter in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment, as well as a spatial vector-autoregressive (SVAR) model. We compare the short-run forecasting performance of these methods, and in particular, we carry out a sensitivity analysis in order to investigate if different number and size of the administrative regions influence their relative forecasting performance. We compute short-run unemployment forecasts in two countries with different administrative territorial divisions and data frequency: Switzerland (26 regions, monthly data for 34 years) and Spain (47 regions, quarterly data for 32 years)

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Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 15_12.

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Date of creation: Jun 2012
Date of revision: Oct 2012
Publication status: Published in E. Fernández-Vazquez, F. Rubiera-Morollón (2012) Defining the Spatial Scale in Modern Regional Analysis: New Challenges from Data at Local Level. Springer Verlag, Berlin, pp. 173-92
Handle: RePEc:rim:rimwps:15_12
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  6. Konstantin Arkadievich Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2008. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German L�nder," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(2), pages 195-207.
  7. Roberto Patuelli & Simonetta Longhi & Aura Reggiani & Peter Nijkamp, 2008. "Neural networks and genetic algorithms as forecasting tools: a case study on German regions," Environment and Planning B: Planning and Design, Pion Ltd, London, vol. 35(4), pages 701-722, July.
  8. Pan, Zheng & LeSage, James P., 1995. "Using spatial contiguity as prior information in vector autoregressive models," Economics Letters, Elsevier, vol. 47(2), pages 137-142, February.
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  15. Giacomini, Raffaella & Granger, Clive W. J., 2004. "Aggregation of space-time processes," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 7-26.
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  17. Daniel A Griffith, 2008. "Spatial-filtering-based contributions to a critique of geographically weighted regression (GWR)," Environment and Planning A, Pion Ltd, London, vol. 40(11), pages 2751-2769, November.
  18. Di Giacinto, Valter, 2002. "Differential regional effects of monetary policy: a geographical SVAR approach," ERSA conference papers ersa02p257, European Regional Science Association.
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