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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, Center For Economic Research)

  • 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," Discussion Paper 2009-92, Tilburg University, Center for Economic Research.
  • Handle: RePEc:tiu:tiucen:d473cc67-03f6-4389-9a9f-3a299fa25c70
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    References listed on IDEAS

    as
    1. 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.
    2. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2008. "Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large," Journal of Econometrics, Elsevier, vol. 146(1), pages 118-134, September.
    3. 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.
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    5. Harry H. Kelejian & Dennis P. Robinson, 1993. "A Suggested Method Of Estimation For Spatial Interdependent Models With Autocorrelated Errors, And An Application To A County Expenditure Model," Papers in Regional Science, Wiley Blackwell, vol. 72(3), pages 297-312, July.
    6. repec:dgr:rugsom:03c27 is not listed on IDEAS
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    Cited by:

    1. Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2019. "A time-space dynamic panel data model with spatial moving average errors," Regional Science and Urban Economics, Elsevier, vol. 76(C), pages 13-31.
    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.
    4. Mi Lin & Yum K. Kwan, 2017. "FDI Spatial Spillovers in China," The World Economy, Wiley Blackwell, vol. 40(8), pages 1514-1530, August.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. J. Elhorst, 2012. "Dynamic spatial panels: models, methods, and inferences," Journal of Geographical Systems, Springer, vol. 14(1), pages 5-28, January.
    11. 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.
    12. 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.
    13. Lin, Mi & Kwan, Yum K., 2014. "FDI Spatial Spillovers in China," MPRA Paper 60754, University Library of Munich, Germany.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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

    Keywords

    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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