Abstract

This paper considers the generalized spatial panel data model with serial correlation proposed by Lee and Yu (Spatial panels: random components versus fixed effects. International Economic Review 2012; 53: 1369–1412.), which encompasses many of the spatial panel data models considered in the literature, and derives the best linear unbiased predictor (BLUP) for that model. This in turn provides valuable BLUP for several spatial panel models as Special Cases. Copyright © 2016 John Wiley & Sons, Ltd.

Highlights

  • This paper considers the generalized spatial panel data model with serial correlation proposed by Lee and Yu (2012) which encompasses a lot of the spatial panel data models considered in the literature, and derives the best linear unbiased predictor (BLUP) for that model

  • Following Frees and Miller (2004) among others, we summarize the accuracy of the forecasts using the mean absolute error (MAE)

  • This paper derives Goldberger’s (1962) best linear unbiased predictor (BLUP) for the generalized spatial panel data model with serial correlation proposed by Lee and Yu (2012)

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Summary

Introduction

Panel data has been used in forecasting gasoline demand across OECD countries, see Baltagi and Griffin (1997); Residential electricity and natural-gas demand using a panel of American states, see Maddala, Trost, Li and Joutz (1997); World carbon dioxide emissions, see Schmalensee, Stoker and Judson (1998); Growth rates of OECD countries, see Hoogstrate, Palm and Pfann (2000); Cigarette sales using a panel of American states, see Baltagi and Li (2004); The impact of uncertainty on U.K. investment authorizations using a panel of U.K. industries, see Driver, Imai, Temple and Urga (2004); Sale of state lottery tickets using panel data on postal (ZIP) codes, see Frees and Miller (2004); Exchange rate determination using industrialized countries quarterly panel data, see Rapach and Wohar (2004); Migration to Germany from 18 source countries over the period 1967-2001, see Brucker and Siliverstovs (2006); Short-term forecasts of employment in a panel of 326 West German regional labor markets observed over the period 1987-2002, see Longhi and Nijkamp (2007); Annual growth rates of real gross regional product for a panel of Chinese regions, see Girardin and Kholodilin (2011), to mention a few. Baltagi and Li (1992) extended this prediction to the case of an error component panel model with serial correlation in the remainder disturbance term. This paper considers the generalized spatial panel data model with serial correlation proposed by Lee and Yu (2012) which encompasses a lot of the spatial panel data models considered in the literature, and derives the best linear unbiased predictor (BLUP) for that model. This in turn provides valuable BLUP for several spatial panel models as special cases. The BLUP for these special cases are shown to follow from our BLUP derivation for the generalized model

The Model
Monte Carlo Simulation
Conclusion
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