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Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors

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Abstract

We extend the three-step generalized methods of moments (GMM) approach of Kapoor, Kelejian, and Prucha (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory variables. Combining the extended Kapoor, Kelejian, and Prucha (2007) approach with the dynamic panel data model GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) and supplementing the dynamic instruments by lagged and weighted exogenous variables as suggested by Kelejian and Robinson (1993) yields new spatial dynamic panel data estimators. The performance of these spatial dynamic panel data estimators is in- vestigated by means of Monte Carlo simulations. We show that di erences in bias as well as root mean squared error between spatial GMM estimates and corresponding GMM estimates in which spatial error correlation is ignored are small.

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Bibliographic Info

Paper provided by International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University in its series International Center for Public Policy Working Paper Series, at AYSPS, GSU with number paper0915.

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Length: 40 pages
Date of creation: 01 Dec 2009
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Handle: RePEc:ays:ispwps:paper0915

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Keywords: Dynamic panel models; spatial lag; spatial error; GMM estimation;

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Cited by:
  1. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2011. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," SERC Discussion Papers 0095, Spatial Economics Research Centre, LSE.
  2. J. Elhorst, 2012. "Dynamic spatial panels: models, methods, and inferences," Journal of Geographical Systems, Springer, vol. 14(1), pages 5-28, January.

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