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 InfoPaper provided by ERMES, University Paris 2 in its series Working Papers ERMES with number 0710.
Date of creation: 2007
Date of revision:
Other versions of this item:
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
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