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Long term regional forecasting with spatial equation systems

Author

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  • Wolfgang Polasek

    (Institute for Advanced Studies,Vienna, Austria and The Rimini Centre for Economic Analysis, Rimini, Italy)

  • Richard Sellner

    (Institute for Advanced Studies,Vienna, Austria)

  • Wolfgang Schwarzbauer

    (Institute for Advanced Studies,Vienna, Austria)

Abstract

Long-term predictions with a system of dynamic panel models can have tricky properties since the time dimension in regional (cross) sectional models is usually short. This paper describes the possible approaches to make long-term-ahead forecast based on a dynamic panel set, where the dependent variable is a cross-sectional vector of growth rates. Since the variance of the forecasts will depend on number of updating steps, we compare the forecasts behavior of a aggregated and a disaggregated updating procedure. The cross section of the panel data can be modeled by a spatial AR (SAR) or Durbin model, including heteroscedasticity. Since the forecasts are non-linear functions of the model parameters we show what MCMC based approach will produce the best results. We demonstrate the approach by a example where we have to predict 20 years ahead of regional growth in 99 Austrian regions in a space-time dependent system of equations.

Suggested Citation

  • Wolfgang Polasek & Richard Sellner & Wolfgang Schwarzbauer, 2007. "Long term regional forecasting with spatial equation systems," Working Paper series 10_07, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:10_07
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    Cited by:

    1. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.

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