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

Arbeitspapier

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).

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Quaderni - Working Paper DSE ; No. 835

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Forecasting Models; Simulation Methods ; Macroeconomics: Consumption, Saving, Production, Employment, and Investment: Forecasting and Simulation: Models and Applications ; Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity ; Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Mayor-Fernández, Matías ; Patuelli, Roberto
• Ereignis: Veröffentlichung
• (wer): Alma Mater Studiorum - Università di Bologna, Dipartimento di Scienze Economiche (DSE)
• (wo): Bologna
• (wann): 2012

Weitere Informationen

• DOI: doi:10.6092/unibo/amsacta/4179
• Handle: http://hdl.handle.net/10419/159674
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ