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

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Author Info

  • 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)

Abstract

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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Bibliographic Info

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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Keywords: regional forecasts; spatial econometrics; dynamic panel; SVAR;

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References

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  1. 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.
  2. Hampel, Katharina & Kunz, Marcus & Schanne, Norbert & Wapler, Rüdiger & Weyh, Antje, 2007. "Regional employment forecasts with spatial interdependencies," IAB Discussion Paper 200702, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  3. M. Hashem Pesaran & Til Schuermann & Scott M. Weiner, 2001. "Modelling regional interdependencies using a global error-correcting macroeconometric model," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 B4-1, International Conferences on Panel Data.
  4. Todd H. Kuethe & Valerien Pede, 2009. "Regional Housing Price Cycles: A Spatio-Temporal Analysis Using Us State Level," Working Papers 09-04, Purdue University, College of Agriculture, Department of Agricultural Economics.
  5. Overman, Henry G. & Puga, Diego, 1999. "Unemployment Clusters Across European Regions and Countries," CEPR Discussion Papers 2255, C.E.P.R. Discussion Papers.
  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. 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.
  8. Roberto Patuelli & Norbert Schanne & Daniel A. Griffith & Peter Nijkamp, 2009. "Persistence of Regional Unemployment: Application of a Spatial Filtering Approach to Local Labour Markets in Germany," Working Paper Series 49_09, The Rimini Centre for Economic Analysis, revised Nov 2011.
  9. Eleonora Patacchini & Yves Zenou, 2007. "Spatial dependence in local unemployment rates," Journal of Economic Geography, Oxford University Press, vol. 7(2), pages 169-191, March.
  10. 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.
  11. 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.
  12. Bernard Fingleton, 2008. "Prediction Using Panel Data Regression with Spatial Random Effects," SERC Discussion Papers 0007, Spatial Economics Research Centre, LSE.
  13. Jimeno, Juan F. & Bentolila, Samuel, 1998. "Regional unemployment persistence (Spain, 1976-1994)," Labour Economics, Elsevier, vol. 5(1), pages 25-51, March.
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  15. Di Giacinto, Valter, 2002. "Differential regional effects of monetary policy: a geographical SVAR approach," ERSA conference papers ersa02p257, European Regional Science Association.
  16. 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.
  17. Roberto Patuelli & Simonetta Longhi & Aura Reggiani & Peter Nijkamp, 2005. "Forecasting Regional Employment in Germany by Means of Neural Networks and Genetic Algorithms," Computational Economics 0511002, EconWPA.
  18. Konstantin A. Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2007. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder," Discussion Papers of DIW Berlin 664, DIW Berlin, German Institute for Economic Research.
  19. Michael Beenstock & Daniel Felsenstein, 2007. "Spatial Vector Autoregressions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 2(2), pages 167-196.
  20. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  21. 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.
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Cited by:
  1. Schanne, Norbert, 2012. "The formation of experts' expectations on labour markets : do they run with the pack?," IAB Discussion Paper 201225, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

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