Estimating and forecasting with a dynamic spatial panel data model

Badi Baltagi, Bernard Fingleton () and Alain Pirotte

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: panel data; spatial lag; error components; linear predictor; GMM; spatial autocorrelation (search for similar items in EconPapers)
JEL-codes: C33 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2011-11
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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http://eprints.lse.ac.uk/58322/ Open access version. (application/pdf)

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 (2012)
Working Paper: Estimating and Forecasting with a Dynamic Spatial Panel Data Model (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:58322

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