Forecasting with spatial panel data
AbstractVarious forecasts using panel data with spatial error correlation are compared using Monte Carlo experiments. The true data generating process is assumed to be a simple error component regression model with spatial remainder disturbances of the autoregressive or moving average type. The best linear unbiased predictor is compared with other forecasts ignoring spatial correlation, or ignoring heterogeneity due to the individual effects. In addition, the root mean squared error performance of these forecasts is examined under misspecification of the spatial error process, various spatial weight matrices, and heterogeneous rather than homogeneous panel data models.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 56 (2012)
Issue (Month): 11 ()
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Web page: http://www.elsevier.com/locate/csda
Forecasting; BLUP; Panel data; Spatial dependence; Heterogeneity;
Other versions of this item:
- Baltagi B-H & Bresson G. & Pirotte A., 2007. "Forecasting with Spatial Panel Data," Working Papers ERMES 0710, ERMES, University Paris 2.
- Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2009. "Forecasting with Spatial Panel Data," IZA Discussion Papers 4242, Institute for the Study of Labor (IZA).
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
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