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Estimating and forecasting with a dynamic spatial panel data model

Author

Listed:
  • Baltagi, Badi H.
  • Fingleton, Bernard
  • Pirotte, Alain
Abstract
This paper 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 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.

Suggested Citation

  • Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2011. "Estimating and forecasting with a dynamic spatial panel data model," LSE Research Online Documents on Economics 58322, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:58322
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    File URL: http://eprints.lse.ac.uk/58322/
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    References listed on IDEAS

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    More about this item

    Keywords

    panel data; spatial lag; error components; linear predictor; GMM; spatial autocorrelation;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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