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Forecasting regional labor market developments under spatial heterogeneity and spatial correlation

Author

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  • Longhi, Simonetta

    (Vrije Universiteit Amsterdam, Faculteit der Economische Wetenschappen en Econometrie (Free University Amsterdam, Faculty of Economics Sciences, Business Administration and Economitrics)

  • Nijkamp, Peter
Abstract
Because of heterogeneity across regions, economic policy measures are increasingly targeted at the regional level, and the need for forecasts at the regional level is rapidly increasing. The data available to compute regional forecasts is usually based on a pseudo-panel of a limited number of observations over time, and a large number of areas (regions) strongly interacting with each other. The application of traditional time-series techniques to distinct time series of regional data is likely to be a suboptimal forecasting strategy. In the field of regional forecasting of socioeconomic variables, both linear and nonlinear models have recently been applied and evaluated. However, often such analyses ignore the spatial interactions among regions. We evaluate the ability of different statistical techniques - namely spatial error and spatial cross-regressive models - to correct for misspecifications due to neglected spatial correlation in the data. Our empirical application concerns short-term forecasts of employment in 326 West German regions; we find that the superimposed spatial structure that is required for the estimation of spatial models improves the forecasting performance of non-spatial models.

Suggested Citation

  • Longhi, Simonetta & Nijkamp, Peter, 2006. "Forecasting regional labor market developments under spatial heterogeneity and spatial correlation," Serie Research Memoranda 0015, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    • Handle: RePEc:vua:wpaper:2006-15
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    References listed on IDEAS

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    Cited by:

    1. Konstantin Arkadievich Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2008. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(2), pages 195-207.
    2. Ana Angulo & Jesús Mur & Javier Trívez, 2013. "Forecasting heterogeneous regional data: the case of European employment," ERSA conference papers ersa13p953, European Regional Science Association.
    3. Konstantin A. Kholodilin & Andreas Mense, 2012. "Forecasting the Prices and Rents for Flats in Large German Cities," Discussion Papers of DIW Berlin 1207, DIW Berlin, German Institute for Economic Research.

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    More about this item

    Keywords

    Space-Time Data; Regional Forecasts; Spatial Heterogeneity; Spatial Correlation;
    All these keywords.

    JEL classification:

    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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