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Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors




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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  • 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.
  • Handle: RePEc:ays:ispwps:paper0915

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    References listed on IDEAS

    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. 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.
    3. 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.
    4. repec:dgr:rugsom:03c27 is not listed on IDEAS
    5. 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).
    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, February.
    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, June.
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    Cited by:

    1. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    2. Cizek, P. & Jacobs, J. & Ligthart, J.E. & Vrijburg, H., 2015. "GMM Estimation of Fixed Effects Dynamic Panel Data Models with Spatial Lag and Spatial Errors (Revised version of CentER DP 2011-134)," Discussion Paper 2015-003, Tilburg University, Center for Economic Research.
    3. Cizek, P. & Jacobs, J.P.A.M. & Ligthart, J.E. & Vrijburg, H., 2011. "GMM Estimation of Fixed Effects Dynamic Panel Data Models with Spatial Lag and Spatial Errors (Replaced by CentER DP 2015-003)," Discussion Paper 2011-134, Tilburg University, Center for Economic Research.
    4. repec:eee:rensus:v:81:y:2018:i:p2:p:2935-2946 is not listed on IDEAS
    5. Kazuhiko Hayakawa & M. Hashem Pesaran & L. Vanessa Smith, 2014. "Transformed Maximum Likelihood Estimation of Short Dynamic Panel Data Models with Interactive Effects," CESifo Working Paper Series 4822, CESifo Group Munich.
    6. J. Elhorst, 2012. "Dynamic spatial panels: models, methods, and inferences," Journal of Geographical Systems, Springer, vol. 14(1), pages 5-28, January.
    7. bouayad-agha-Hamouche, salima & turpin, nadine & védrine, lionel, 2012. "Fostering the potential endogenous development of European regions: a spatial dynamic panel data analysis of the Cohesion Policy," MPRA Paper 65470, University Library of Munich, Germany.
    8. repec:unt:jnapdj:v:24:y:2017:i:2:p:113-139 is not listed on IDEAS
    9. David Bartolini & Raffaella Santolini, 2012. "Political yardstick competition among Italian municipalities on spending decisions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 49(1), pages 213-235, August.
    10. Lin, Mi & Kwan, Yum K., 2016. "FDI technology spillovers, geography, and spatial diffusion," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 257-274.
    11. Zheng, Xinye & Li, Fanghua & Song, Shunfeng & Yu, Yihua, 2013. "Central government's infrastructure investment across Chinese regions: A dynamic spatial panel data approach," China Economic Review, Elsevier, vol. 27(C), pages 264-276.
    12. Lin, Mi & Kwan, Yum K., 2014. "FDI Spatial Spillovers in China," MPRA Paper 60754, University Library of Munich, Germany.
    13. Mohanty, Biswajit & Bhanumurthy, N. R. & Dastidar, Ananya Ghosh, 2017. "What explains Regional Imbalances in Infrastructure?: Evidence from Indian States," Working Papers 17/197, National Institute of Public Finance and Policy.
    14. Zheng, Xinye & Yu, Yihua & Wang, Jing & Deng, Huihui, 2013. "Identifying the determinants and spatial nexus of provincial carbon intensity in China: A dynamic spatial panel approach," MPRA Paper 56088, University Library of Munich, Germany.

    More about this item


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

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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