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Estimating and Forecasting with a Dynamic Spatial Panel Data Model

  • Badi H. Baltagi
  • Bernard Fingleton
  • Alain Pirotte

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.

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Paper provided by Spatial Economics Research Centre, LSE in its series SERC Discussion Papers with number 0095.

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Date of creation: Nov 2011
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Handle: RePEc:cep:sercdp:0095
Contact details of provider: Web page: http://www.spatialeconomics.ac.uk/SERC/publications/default.asp

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  1. 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.
  2. Richard Blundell & Steve Bond & Frank Windmeijer, 2000. "Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator," IFS Working Papers W00/12, Institute for Fiscal Studies.
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  4. Arellano, Manuel & Honore, Bo, 2001. "Panel data models: some recent developments," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 53, pages 3229-3296 Elsevier.
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  6. Kukenova, Madina & Monteiro, Jose-Antonio, 2008. "Spatial Dynamic Panel Model and System GMM: A Monte Carlo Investigation," MPRA Paper 11569, University Library of Munich, Germany, revised Nov 2008.
  7. Lee, Lung-fei & Yu, Jihai, 2010. "Some recent developments in spatial panel data models," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 255-271, September.
  8. Konstantin Arkadievich Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2008. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German L�nder," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(2), pages 195-207.
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  11. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Wiley Blackwell, vol. 58(2), pages 277-97, April.
  12. Yu, Jihai & Lee, Lung-fei, 2010. "Estimation Of Unit Root Spatial Dynamic Panel Data Models," Econometric Theory, Cambridge University Press, vol. 26(05), pages 1332-1362, October.
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  20. Bernard Fingleton, 2008. "Prediction using panel data regression with spatial random effects," LSE Research Online Documents on Economics 33150, London School of Economics and Political Science, LSE Library.
  21. Jacobs, J.P.A.M. & Ligthart, J.E. & Vrijburg, H., 2009. "Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors," Discussion Paper 2009-92, Tilburg University, Center for Economic Research.
  22. Fingleton, Bernard, 2010. "Predicting the Geography of House Prices," MPRA Paper 21113, University Library of Munich, Germany.
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  25. 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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  27. Wilfried Koch, 2008. "Development Accounting with Spatial Effects," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(3), pages 321-342.
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