Estimating and Forecasting With A Dynamic Spatial Panel Data Model

Badi Baltagi, Bernard Fingleton () and Alain Pirotte
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Alain Pirotte: Université Panthéon-Assas Paris II

No 149, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

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. Key Words: Forecasting, Spatial Correlation, Panel Data, Dynamic Models JEL No. C33

Pages: 33 pages
Date: 2012-12
New Economics Papers: this item is included in nep-for and nep-ure
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Citations: View citations in EconPapers (26)

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Related works:
Journal Article: Estimating and Forecasting with a Dynamic Spatial Panel Data Model (2014)
Working Paper: Estimating and Forecasting with a Dynamic Spatial Panel Data Model (2011)
Working Paper: Estimating and forecasting with a dynamic spatial panel data model (2011)
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