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A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder

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  • Konstantin Arkadievich Kholodilin
  • Boriss Siliverstovs
  • Stefan Kooths

Abstract

Abstract In this paper, we make multi-step forecasts of the annual growth rates of the real GDP for each of the 16 German Länder simultaneously. We apply dynamic panel models accounting for spatial dependence between regional GDP. We find that both pooling and accounting for spatial effects help to improve the forecast performance substantially. We demonstrate that the effect of accounting for spatial dependence is more pronounced for longer forecasting horizons (the forecast accuracy gain is about 9% for a 1-year horizon and exceeds 40% for a 5-year horizon). We recommend incorporating a spatial dependence structure into regional forecasting models, especially when long-term forecasts are made.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:specan:v:3:y:2008:i:2:p:195-207
    DOI: 10.1080/17421770801996656
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    14. Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 175-185.
    15. Herbert Brücker & Boriss Siliverstovs, 2006. "On the estimation and forecasting of international migration: how relevant is heterogeneity across countries?," Empirical Economics, Springer, vol. 31(3), pages 735-754, September.
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    21. Hinze, Jorg, 2003. "Prognoseleistung von Fruhindikatoren: Die Bedeutung von Fruhindikatoren fur Konjunk-turprognosen - Eine Analyse fur Deutschland," Discussion Paper Series 26253, Hamburg Institute of International Economics.
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    More about this item

    Keywords

    German Länder; forecasting; dynamic panel model; spatial autocorrelation; C21; C23; C53;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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