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)
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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.
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Paper provided by Rimini Centre for Economic Analysis in its series Working Paper Series with number
10-07.