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Forecasting Regional Labor Market Developments under Spatial Autocorrelation

Author

Listed:
  • Simonetta Longhi

    (Institute for Social and Economic Research, University of Essex, Colchester, UK, slonghi@essex.ac.uk)

  • Peter Nijkamp

    (Department of Spatial Economics, Free University Amsterdam, the Netherlands, pnijkamp@feweb.vu.nl)

Abstract

Because of heterogeneity across regions, economic policy measures are increasingly targeted at the regional level and, therefore, require regional forecasts. The data available to compute regional forecasts are usually a pseudo panel of a limited number of observations over time and a large number of regions strongly interacting with each other. Traditional time-series techniques applied to distinct time series of regional data are probably a suboptimal forecasting strategy. Although both linear and nonlinear models have been applied and evaluated to forecast socioeconomic variables, spatial interactions among regions are often ignored. This article evaluates the ability of spatial error and spatial lag models to correct for misspecifications due to neglected spatial autocorrelation in the data. The empirical application on short-term forecasts of employment in 326 West German regions shows that the superimposed spatial structure that is required for the estimation of spatial models improves the forecasting performance of nonspatial models.

Suggested Citation

  • Simonetta Longhi & Peter Nijkamp, 2007. "Forecasting Regional Labor Market Developments under Spatial Autocorrelation," International Regional Science Review, , vol. 30(2), pages 100-119, April.
  • Handle: RePEc:sae:inrsre:v:30:y:2007:i:2:p:100-119
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    References listed on IDEAS

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