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Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors

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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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File URL: http://icepp.gsu.edu/files/2015/03/ispwp0915.pdf
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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
Handle: RePEc:ays:ispwps:paper0915
Contact details of provider: Phone: 404-413-0235
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Web page: http://aysps.gsu.edu/isp/index.html

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  1. 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.
  2. Harald Badinger & Werner Muller & Gabriele Tondl, 2004. "Regional Convergence in the European Union, 1985- 1999: A Spatial Dynamic Panel Analysis," Regional Studies, Taylor & Francis Journals, vol. 38(3), pages 241-253.
  3. repec:dgr:rugsom:03c27 is not listed on IDEAS
  4. Elhorst, J. Paul, 2003. "Unconditional maximum likelihood estimation of dynamic models for spatial panels," Research Report 03C27, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
  5. Jan Jacobs & Jenny Ligthart & Hendrik Vrijburg, 2010. "Consumption tax competition among governments: Evidence from the United States," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 17(3), pages 271-294, June.
  6. Won Kim, Chong & Phipps, Tim T. & Anselin, Luc, 2003. "Measuring the benefits of air quality improvement: a spatial hedonic approach," Journal of Environmental Economics and Management, Elsevier, vol. 45(1), pages 24-39, January.
  7. Case, Anne C. & Rosen, Harvey S. & Hines, James Jr., 1993. "Budget spillovers and fiscal policy interdependence : Evidence from the states," Journal of Public Economics, Elsevier, vol. 52(3), pages 285-307, October.
  8. David Roodman, 2009. "A Note on the Theme of Too Many Instruments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(1), pages 135-158, 02.
  9. Elhorst, J. Paul, 2008. "Serial and spatial error correlation," Economics Letters, Elsevier, vol. 100(3), pages 422-424, September.
  10. J. Barkley Rosser, 2009. "Introduction," Chapters,in: Handbook of Research on Complexity, chapter 1 Edward Elgar Publishing.
  11. Egger, Peter & Pfaffermayr, Michael & Winner, Hannes, 2005. "An unbalanced spatial panel data approach to US state tax competition," Economics Letters, Elsevier, vol. 88(3), pages 329-335, September.
  12. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291.
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