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

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  • Konstantin A. Kholodilin
  • Boriss Siliverstovs
  • Stefan Kooths

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 (states) simultaneously. Beside the usual panel data models, such as pooled and fixed-effects models, we apply panel models that explicitly account for spatial dependence between regional GDP. We find that both pooling and accounting for spatial effects helps substantially improve the forecast performance compared to the individual autoregressive models estimated for each of the Länder separately. More importantly, we have demonstrated that effect of accounting for spatial dependence is even more pronounced at longer forecasting horizons (the forecast accuracy gain as measured by the root mean squared forecast error is about 9% at 1-year horizon and exceeds 40% at 5-year horizon). Hence, we strongly recommend incorporating spatial dependence structure into regional forecasting models, especially, when long-term forecasts are made.

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File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.55747.de/dp664.pdf
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Bibliographic Info

Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 664.

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Length: 19 p.
Date of creation: 2007
Date of revision:
Handle: RePEc:diw:diwwpp:dp664

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Keywords: German Länder; forecasting; dynamic panel model; spatial autocorrelation;

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  8. Konstantin A. Kholodilin & Boriss Siliverstovs, 2005. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Discussion Papers of DIW Berlin 522, DIW Berlin, German Institute for Economic Research.
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  18. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2006. "The Use of Spatial Filtering Techniques: The Spatial and Space-time Structure of German Unemployment Data," Tinbergen Institute Discussion Papers, Tinbergen Institute 06-049/3, Tinbergen Institute.
  19. Badi H. Baltagi & James M. Griffin & Weiwen Xiong, 2000. "To Pool Or Not To Pool: Homogeneous Versus Hetergeneous Estimations Applied to Cigarette Demand," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 117-126, February.
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