Forecasting with Spatial Panel Data
AbstractThis paper compares various forecasts using panel data with spatial error correlation. 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, using Monte Carlo experiments. In addition, we check the performance of these forecasts under misspecification of the spatial error process, various spatial weight matrices, and heterogeneous rather than homogeneous panel data models.
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Bibliographic InfoPaper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 4242.
Length: 37 pages
Date of creation: Jun 2009
Date of revision:
Publication status: published in: Computational Statistics and Data Analysis, 2012, 56 (11), 3381–3397
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Other versions of this item:
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-07-11 (All new papers)
- NEP-ECM-2009-07-11 (Econometrics)
- NEP-FOR-2009-07-11 (Forecasting)
- NEP-GEO-2009-07-11 (Economic Geography)
- NEP-URE-2009-07-11 (Urban & Real Estate Economics)
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