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Estimating and Forecasting With A Dynamic Spatial Panel Data Model

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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

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Paper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 149.

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Length: 33 pages
Date of creation: Dec 2012
Date of revision:
Handle: RePEc:max:cprwps:149

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  1. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2009. "Forecasting with Spatial Panel Data," IZA Discussion Papers 4242, Institute for the Study of Labor (IZA).
  2. Bernard Fingleton, 2008. "Prediction Using Panel Data Regression with Spatial Random Effects," SERC Discussion Papers 0007, Spatial Economics Research Centre, LSE.
  3. Blundell, R. & Bond, S., 1995. "Initial Conditions and Moment Restrictions in Dynamic Panel Data Models," Economics Papers 104, Economics Group, Nuffield College, University of Oxford.
  4. Baltagi, Badi H. & Pirotte, Alain, 2010. "Panel data inference under spatial dependence," Economic Modelling, Elsevier, vol. 27(6), pages 1368-1381, November.
  5. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
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  7. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-33, May.
  8. Jan P.A.M. Jacobs & Jenny E. Ligthart & Hendrik Vrijburg, 2009. "Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors," International Center for Public Policy Working Paper Series, at AYSPS, GSU paper0915, International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University.
  9. Bernard Fingleton, 2010. "Predicting the Geography of House Prices," SERC Discussion Papers 0045, Spatial Economics Research Centre, LSE.
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  15. G�ran Therborn & K.C. Ho, 2009. "Introduction," City, Taylor & Francis Journals, vol. 13(1), pages 53-62, March.
  16. Badi H. Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: The Case of Liquor," Center for Policy Research Working Papers 84, Center for Policy Research, Maxwell School, Syracuse University.
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  22. Salima Bouayad-Agha & Lionel V�drine, 2010. "Estimation Strategies for a Spatial Dynamic Panel using GMM. A New Approach to the Convergence Issue of European Regions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(2), pages 205-227.
  23. Bernard Fingleton & Manfred Fischer, 2010. "Neoclassical theory versus new economic geography: competing explanations of cross-regional variation in economic development," The Annals of Regional Science, Springer, vol. 44(3), pages 467-491, June.
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Cited by:
  1. Atems, Bebonchu, 2013. "The spatial dynamics of growth and inequality: Evidence using U.S. county-level data," Economics Letters, Elsevier, vol. 118(1), pages 19-22.

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