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Spatial dynamic panel data models with random effects

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  • Parent, Olivier
  • LeSage, James P.

Abstract

We develop a general space–time filter applied to panel data models in order to control for heterogeneity as well as both time and spatial dependence. Treatment of initial period observations is analyzed when the number of time periods is small. A second issue relates to a restriction implied by the filter specification on the space–time cross-product term that can greatly simplify interpretation of model estimates as well as the estimation procedure. An applied illustration of the method is provided using a Solow growth model. The application shows that the theoretical restriction implied for the cross-product term in our space–time filter specification is consistent with this particular dynamic space–time panel data set.

Suggested Citation

  • Parent, Olivier & LeSage, James P., 2012. "Spatial dynamic panel data models with random effects," Regional Science and Urban Economics, Elsevier, vol. 42(4), pages 727-738.
  • Handle: RePEc:eee:regeco:v:42:y:2012:i:4:p:727-738
    DOI: 10.1016/j.regsciurbeco.2012.04.008
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    References listed on IDEAS

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    1. Parent, Olivier & LeSage, James P., 2010. "A spatial dynamic panel model with random effects applied to commuting times," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 633-645, June.
    2. Alok Bhargava & J. D. Sargan, 2006. "Estimating Dynamic Random Effects Models From Panel Data Covering Short Time Periods," World Scientific Book Chapters,in: Econometrics, Statistics And Computational Approaches In Food And Health Sciences, chapter 1, pages 3-27 World Scientific Publishing Co. Pte. Ltd..
    3. Ciccone, Antonio, 2002. "Agglomeration effects in Europe," European Economic Review, Elsevier, vol. 46(2), pages 213-227, February.
    4. 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.
    5. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2009. "Identification of peer effects through social networks," Journal of Econometrics, Elsevier, vol. 150(1), pages 41-55, May.
    6. Elhorst, J. Paul, 2010. "Dynamic panels with endogenous interaction effects when T is small," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 272-282, September.
    7. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    8. Parent, Olivier & LeSage, James P., 2011. "A space-time filter for panel data models containing random effects," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 475-490, January.
    9. Bun, Maurice J.G. & Kiviet, Jan F., 2006. "The effects of dynamic feedbacks on LS and MM estimator accuracy in panel data models," Journal of Econometrics, Elsevier, vol. 132(2), pages 409-444, June.
    10. LeSage, James P. & Kelley Pace, R., 2007. "A matrix exponential spatial specification," Journal of Econometrics, Elsevier, vol. 140(1), pages 190-214, September.
    11. Baltagi, Badi H. & Heun Song, Seuck & Cheol Jung, Byoung & Koh, Won, 2007. "Testing for serial correlation, spatial autocorrelation and random effects using panel data," Journal of Econometrics, Elsevier, vol. 140(1), pages 5-51, September.
    12. Nicole M. Fortin, 2006. "Higher-Education Policies and the College Wage Premium: Cross-State Evidence from the 1990s," American Economic Review, American Economic Association, vol. 96(4), pages 959-987, September.
    13. Kelejian, Harry & Tavlas, George S. & Petroulas, Pavlos, 2012. "In the neighborhood: The trade effects of the Euro in a spatial framework," Regional Science and Urban Economics, Elsevier, vol. 42(1-2), pages 314-322.
    14. Kiviet, Jan F., 1995. "On bias, inconsistency, and efficiency of various estimators in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 68(1), pages 53-78, July.
    15. Gasper A. Garofalo & Steven Yamarik, 2002. "Regional Convergence: Evidence From A New State-By-State Capital Stock Series," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 316-323, May.
    16. Devereux, M.P. & Lockwood, B. & Redoano, M., 2007. "Horizontal and vertical indirect tax competition: Theory and some evidence from the USA," Journal of Public Economics, Elsevier, vol. 91(3-4), pages 451-479, April.
    17. Blanchard, Pierre & Matyas, Laszlo, 1996. "Robustness of tests for error components models to non-normality," Economics Letters, Elsevier, vol. 51(2), pages 161-167, May.
    18. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    19. Mohl, P. & Hagen, T., 2010. "Do EU structural funds promote regional growth? New evidence from various panel data approaches," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 353-365, September.
    20. Magnus, Jan R., 1982. "Multivariate error components analysis of linear and nonlinear regression models by maximum likelihood," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 239-285, August.
    21. Su, Liangjun & Yang, Zhenlin, 2015. "QML estimation of dynamic panel data models with spatial errors," Journal of Econometrics, Elsevier, vol. 185(1), pages 230-258.
    22. Keller, Wolfgang & Shiue, Carol H., 2007. "The origin of spatial interaction," Journal of Econometrics, Elsevier, vol. 140(1), pages 304-332, September.
    23. Lottmann, Franziska, 2012. "Spatial dependencies in German matching functions," Regional Science and Urban Economics, Elsevier, vol. 42(1-2), pages 27-41.
    24. repec:spr:stemec:978-3-7908-2070-6 is not listed on IDEAS
    25. J. Barkley Rosser, 2009. "Introduction," Chapters,in: Handbook of Research on Complexity, chapter 1 Edward Elgar Publishing.
    26. Korniotis, George M., 2010. "Estimating Panel Models With Internal and External Habit Formation," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 145-158.
    27. 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.
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    Citations

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    Cited by:

    1. Parent, Olivier & LeSage, James P., 2010. "A spatial dynamic panel model with random effects applied to commuting times," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 633-645, June.
    2. Lee, Lung-fei & Yu, Jihai, 2015. "Estimation of fixed effects panel regression models with separable and nonseparable space–time filters," Journal of Econometrics, Elsevier, vol. 184(1), pages 174-192.
    3. Hidalgo, Javier & Schafgans, Marcia, 2017. "Inference and testing breaks in large dynamic panels with strong cross sectional dependence," Journal of Econometrics, Elsevier, vol. 196(2), pages 259-274.
    4. Manfred M. Fischer & James P. LeSage, 2015. "A Bayesian space-time approach to identifying and interpreting regional convergence clubs in Europe," Papers in Regional Science, Wiley Blackwell, vol. 94(4), pages 677-702, November.
    5. Kripfganz, Sebastian, 2014. "Unconditional Transformed Likelihood Estimation of Time-Space Dynamic Panel Data Models," Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100604, Verein für Socialpolitik / German Economic Association.
    6. Corinne Autant-Bernard, 2011. "Spatial econometrics of innovation: Recent contributions and research perspectives," Working Papers halshs-00605056, HAL.
    7. James LeSage, 2015. "Software for Bayesian cross section and panel spatial model comparison," Journal of Geographical Systems, Springer, vol. 17(4), pages 297-310, October.
    8. Michael Alexeev & Yao-Yu Chih, 2017. "Oil Price Shocks and Economic Growth in the Us," Caepr Working Papers 2017-011 Classification-Q, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
    9. Debarsy, Nicolas & Dossougoin, Cyrille & Ertur, Cem & Gnabo, Jean-Yves, 2018. "Measuring sovereign risk spillovers and assessing the role of transmission channels: A spatial econometrics approach," Journal of Economic Dynamics and Control, Elsevier, vol. 87(C), pages 21-45.
    10. Wolter, James Lewis, 2016. "Kernel estimation of hazard functions when observations have dependent and common covariates," Journal of Econometrics, Elsevier, vol. 193(1), pages 1-16.
    11. Baltagi, Badi H. & Egger, Peter H. & Kesina, Michaela, 2017. "Determinants of firm-level domestic sales and exports with spillovers: Evidence from China," Journal of Econometrics, Elsevier, vol. 199(2), pages 184-201.
    12. Philipp Breidenbach & Timo Mitze & Christoph Schmidt, 2011. "Evaluating EU Regional Policy: Many Empirical Specifications, One (Unpleasant) Result," ERSA conference papers ersa11p1144, European Regional Science Association.
    13. Sleuwaegen, Leo & Boiardi, Priscilla, 2014. "Creativity and regional innovation: Evidence from EU regions," Research Policy, Elsevier, vol. 43(9), pages 1508-1522.
    14. James Wolter, 2015. "Kernel Estimation Of Hazard Functions When Observations Have Dependent and Common Covariates," Economics Series Working Papers 761, University of Oxford, Department of Economics.
    15. repec:eee:regeco:v:65:y:2017:i:c:p:65-88 is not listed on IDEAS
    16. Harry H. Kelejian & Gianfranco Piras, 2016. "A J test for dynamic panel model with fixed effects, and nonparametric spatial and time dependence," Empirical Economics, Springer, vol. 51(4), pages 1581-1605, December.
    17. James P. LESAGE, 2014. "Software For Bayesian Spatial Model Comparison," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 40, pages 11-24.
    18. Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2018. "A Time-Space Dynamic Panel Data Model with Spatial Moving Average Errors," MPRA Paper 86371, University Library of Munich, Germany.
    19. repec:elg:eechap:14395_1 is not listed on IDEAS

    More about this item

    Keywords

    Spatial correlation; Dynamic panels; Bayesian estimations;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General

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