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

  • M. Mayor-Fernández
  • R. Patuelli

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 Dipartimento Scienze Economiche, Universita' di Bologna in its series Working Papers with number wp835.

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Date of creation: Jun 2012
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Handle: RePEc:bol:bodewp:wp835
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  2. 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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  4. 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.
  5. Taylor, Jim & Bradley, Steve, 1997. "Unemployment in Europe: A Comparative Analysis of Regional Disparities in Germany, Italy and the UK," Kyklos, Wiley Blackwell, vol. 50(2), pages 221-45.
  6. Lopez-Bazo, Enrique & Del Barrio, Tomas & Artis, Manuel, 2002. "The regional distribution of spanish unemployment. A spatial analysis," ERSA conference papers ersa02p020, European Regional Science Association.
  7. Ana Angulo & F. Trívez, 2010. "The impact of spatial elements on the forecasting of Spanish labour series," Journal of Geographical Systems, Springer, vol. 12(2), pages 155-174, June.
  8. Roberto Patuelli & Norbert Schanne & Daniel A. Griffith & Peter Nijkamp, 2010. "Persistent Disparities in Regional Unemployment: Application of a Spatial Filtering Approach to Local Labour Markets in Germany," Quaderni della facoltà di Scienze economiche dell'Università di Lugano 1001, USI Università della Svizzera italiana.
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  15. Hernandez-Murillo, Ruben & Owyang, Michael T., 2006. "The information content of regional employment data for forecasting aggregate conditions," Economics Letters, Elsevier, vol. 90(3), pages 335-339, March.
  16. Jimeno, Juan F. & Bentolila, Samuel, 1998. "Regional unemployment persistence (Spain, 1976-1994)," Labour Economics, Elsevier, vol. 5(1), pages 25-51, March.
  17. Diego Puga, 1999. "Unemployment clusters across Europe's regions and countries," Working Papers dpuga-99-03, University of Toronto, Department of Economics.
  18. 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.
  19. Giacomini, Raffaella & Granger, Clive W.J., 2001. "Aggregationn of Space-Time Processes," University of California at San Diego, Economics Working Paper Series qt77f76455, Department of Economics, UC San Diego.
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