Abstract
Forecasting economic values in administrative units provides very important information for political, institutional and economic agents for their respective planning processes. A crucial stage is the choice of the econometric method to obtain these future values taking into account the diversity and complexity of real economy. Two aspects may be considered when choosing an econometric specification. Firstly, disparities in economic development and welfare within countries (i.e., at the regional level) are often bigger than between countries (Elhorst 1995; Taylor and Bradley 1997; Ertur and Le Gallo 2003; Patuelli 2007; see, for example, the cases of Germany and Spain), and they often show typical geographical/spatial structures. Secondly, with regard to regional unemployment disparities, policy makers need, in order to correctly target their actions and policies, to understand two aspects of such disparities: (a) the determinants of ‘equilibrium’ unemployment and its variation; and, (b) the region-specific and the cross-regional dynamics of unemployment. On the one hand, the need for an explicit consideration of the existence of spatial interdependence in econometric models, which is consistent with regional science theories asserting the importance of spatial linkages in local economic processes, led to what is nowadays quite a large literature of empirical papers. On the other hand, the temporal perspective of the problem has attracted less attention in spatial models, but should be considered jointly.
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Notes
- 1.
These authors assume that these effects are expanded through the time dimension.
- 2.
Structural VAR models are introduced to incorporate some necessary restrictions which are not tested by statistical tools.
- 3.
Griffith (2008) stresses that the SF-based approach to GWR actually provides superior statistical properties (e.g., with regard to multicollinearity) than the original GWR.
- 4.
Moran test results are available from the authors upon request.
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Appendix
Appendix
Histograms of MSE, MAE and MAPE of SVAR (above) and SF (below) models for Switzerland
Histograms of MSE, MAE and MAPE of SVAR (above) and SF (below) models for Spain
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Mayor, M., Patuelli, R. (2012). Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions. In: Fernández Vázquez, E., Rubiera Morollón, F. (eds) Defining the Spatial Scale in Modern Regional Analysis. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31994-5_9
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