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Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors

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  • Jacobs, J.P.A.M.
  • Ligthart, J.E.

    (Tilburg University, School of Economics and Management)

  • Vrijburg, H.

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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Suggested Citation

  • Jacobs, J.P.A.M. & Ligthart, J.E. & Vrijburg, H., 2009. "Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors," Other publications TiSEM d473cc67-03f6-4389-9a9f-3, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:d473cc67-03f6-4389-9a9f-3a299fa25c70
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    Cited by:

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    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)," Other publications TiSEM b4bbf44a-7834-491d-94c8-6, Tilburg University, School of Economics and Management.
    3. 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.
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    5. 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.
    6. 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)," Other publications TiSEM b80cf367-c435-4f20-8e4c-8, Tilburg University, School of Economics and Management.
    7. Zhonghua Cheng & Xiaowen Hu, 2023. "The effects of urbanization and urban sprawl on CO2 emissions in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(2), pages 1792-1808, February.
    8. Cheng, Zhonghua & Li, Lianshui & Liu, Jun, 2018. "Industrial structure, technical progress and carbon intensity in China's provinces," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2935-2946.
    9. J. Paul Elhorst, 2014. "Dynamic Spatial Panels: Models, Methods and Inferences," SpringerBriefs in Regional Science, in: Spatial Econometrics, edition 127, chapter 0, pages 95-119, Springer.
    10. Salima Bouayad-Agha & Lionel Védrine, 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.
    11. Lin, Mi & Kwan, Yum K., 2014. "FDI Spatial Spillovers in China," MPRA Paper 60754, University Library of Munich, Germany.
    12. Salima Bouayad-Agha & Nadine Turpin & Lionel V�drine, 2013. "Fostering the Development of European Regions: A Spatial Dynamic Panel Data Analysis of the Impact of Cohesion Policy," Regional Studies, Taylor & Francis Journals, vol. 47(9), pages 1573-1593, October.
    13. Seydou Coulibaly & Abdramane Camara, 2021. "Working Paper 354 - Taxation, Foreign Direct Investment and Spillover Effects in the Mining Sector," Working Paper Series 2480, African Development Bank.
    14. 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.
    15. Zhonghua Cheng & Qingfei Xu & Ian Fraser Sanderson, 2021. "China's economic growth and haze pollution control," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2653-2669, July.
    16. 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.
    17. 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.
    18. Zhonghua Cheng & Xiai Shi, 2018. "Can Industrial Structural Adjustment Improve the Total-Factor Carbon Emission Performance in China?," IJERPH, MDPI, vol. 15(10), pages 1-20, October.
    19. Mi Lin & Yum K. Kwan, 2017. "FDI Spatial Spillovers in China," The World Economy, Wiley Blackwell, vol. 40(8), pages 1514-1530, August.
    20. Jianhuan Huang & Yue Hua, 2019. "Eco-efficiency Convergence and Green Urban Growth in China," International Regional Science Review, , vol. 42(3-4), pages 307-334, May.
    21. Biswajit Mohanty & N.R. Bhanumurthy & Ananya Ghosh Dastidar, 2017. "What explains regional imbalances in public infrastructure expenditure? Evidence from Indian states," Asia-Pacific Development Journal, United Nations Economic and Social Commission for Asia and the Pacific (ESCAP), vol. 24(2), pages 113-139, December.
    22. Hua, Yue & Xie, Rui & Su, Yaqin, 2018. "Fiscal spending and air pollution in Chinese cities: Identifying composition and technique effects," China Economic Review, Elsevier, vol. 47(C), pages 156-169.
    23. 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.
    24. 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.
    25. Taotao Deng & Yukun Hu, 2019. "Modelling China’s outbound tourist flow to the ‘Silk Road’: A spatial econometric approach," Tourism Economics, , vol. 25(8), pages 1167-1181, December.

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    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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