Estimating and forecasting with a dynamic spatial panel data model

Badi H. Baltagi, Bernard Fingleton, Alain Pirotte

Research output: Contribution to journalArticlepeer-review

76 Scopus citations


This study focuses on the estimation and predictive performance of several estimators for the dynamic and autoregressive spatial lag panel data model with spatially correlated disturbances. In the spirit of Arellano and Bond (1991) and Mutl (2006), a dynamic spatial generalized method of moments (GMM) estimator is proposed based on Kapoor, Kelejian and Prucha (2007) for the spatial autoregressive (SAR) error model. The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a linear predictor of this spatial dynamic model is derived. Using Monte Carlo simulations, we compare the performance of the GMM spatial estimator to that of spatial and non-spatial estimators and illustrate our approach with an application to new economic geography.

Original languageEnglish (US)
Pages (from-to)112-138
Number of pages27
JournalOxford Bulletin of Economics and Statistics
Issue number1
StatePublished - Feb 2014

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


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