This paper was presented at the Economics Seminar Series, Strathclyde University, Glasgow, Scotland, 30 March 2011. Also, at the Vth Conference of the Spatial Econometrics Association, Toulouse University, France, 6–8 July, 2011. We would like to thank Patrick Sevestre, the participants of this seminar and this conference for their useful comments and suggestions, and also the referees and editor of the journal, Anindya Banerjee, for their help and advice.
Estimating and Forecasting with a Dynamic Spatial Panel Data Model*
Article first published online: 9 JAN 2013
© 2013 The Department of Economics, University of Oxford and John Wiley & Sons Ltd.
Oxford Bulletin of Economics and Statistics
Volume 76, Issue 1, pages 112–138, February 2014
How to Cite
Baltagi, B. H., Fingleton, B. and Pirotte, A. (2014), Estimating and Forecasting with a Dynamic Spatial Panel Data Model. Oxford Bulletin of Economics and Statistics, 76: 112–138. doi: 10.1111/obes.12011
- Issue published online: 9 JAN 2014
- Article first published online: 9 JAN 2013
- Final Manuscript Received: xxxx
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.