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Spatial dynamic panel data models with correlated random effects

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  • Li, Liyao
  • Yang, Zhenlin

Abstract

In this paper, M-estimation and inference methods are developed for spatial dynamic panel data models with correlated random effects, based on short panels. The unobserved individual-specific effects are assumed to be correlated with the observed time-varying regressors linearly or in a linearizable way, giving the so-called correlated random effects model, which allows the estimation of effects of time-invariant regressors. The unbiased estimating functions are obtained by adjusting the conditional quasi-scores given the initial observations, leading to M-estimators that are consistent, asymptotically normal, and free from the initial conditions except the process starting time. By decomposing the estimating functions into sums of terms uncorrelated given idiosyncratic errors, a hybrid method is developed for consistently estimating the variance–covariance matrix of the M-estimators, which again depends only on the process starting time. Monte Carlo results demonstrate that the proposed methods perform well in finite sample. An empirical application on the political competition in China is presented.

Suggested Citation

  • Li, Liyao & Yang, Zhenlin, 2021. "Spatial dynamic panel data models with correlated random effects," Journal of Econometrics, Elsevier, vol. 221(2), pages 424-454.
  • Handle: RePEc:eee:econom:v:221:y:2021:i:2:p:424-454
    DOI: 10.1016/j.jeconom.2020.05.016
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    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    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. Newey, Whitney K, 1991. "Uniform Convergence in Probability and Stochastic Equicontinuity," Econometrica, Econometric Society, vol. 59(4), pages 1161-1167, July.
    4. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037.
    5. 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.
    6. Hausman, Jerry A & Taylor, William E, 1981. "Panel Data and Unobservable Individual Effects," Econometrica, Econometric Society, vol. 49(6), pages 1377-1398, November.
    7. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    8. Lee, Lung-fei & Yu, Jihai, 2014. "Efficient GMM estimation of spatial dynamic panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 180(2), pages 174-197.
    9. Nicolas Debarsy, 2012. "The Mundlak Approach in the Spatial Durbin Panel Data Model," Spatial Economic Analysis, Taylor & Francis Journals, vol. 7(1), pages 109-131, March.
    10. Li, Hongbin & Zhou, Li-An, 2005. "Political turnover and economic performance: the incentive role of personnel control in China," Journal of Public Economics, Elsevier, vol. 89(9-10), pages 1743-1762, September.
    11. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    12. Yang, Zhenlin, 2018. "Unified M-estimation of fixed-effects spatial dynamic models with short panels," Journal of Econometrics, Elsevier, vol. 205(2), pages 423-447.
    13. 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.
    14. Lee, Lung-Fei, 2002. "Consistency And Efficiency Of Least Squares Estimation For Mixed Regressive, Spatial Autoregressive Models," Econometric Theory, Cambridge University Press, vol. 18(2), pages 252-277, April.
    15. Hubert Jayet & Julie Le Gallo & Luc Anselin, 2008. "Spatial Econometrics and Panel Data Models," Post-Print hal-02389412, HAL.
    16. 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.
    17. Yu, Jihai & Lee, Lung-fei, 2010. "Estimation Of Unit Root Spatial Dynamic Panel Data Models," Econometric Theory, Cambridge University Press, vol. 26(5), pages 1332-1362, October.
    18. Zhenlin Yang & Jihai Yu & Shew Fan Liu, 2015. "Bias correction for fixed effects spatial panel data models," Working Papers 04-2015, Singapore Management University, School of Economics.
    19. Lung‐fei Lee & Jihai Yu, 2016. "Identification of Spatial Durbin Panel Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 133-162, January.
    20. 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.
    21. Xi Qu & Xiaoliang Wang & Lung‐fei Lee, 2016. "Instrumental variable estimation of a spatial dynamic panel model with endogenous spatial weights when T is small," Econometrics Journal, Royal Economic Society, vol. 19(3), pages 261-290, October.
    22. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    23. Yang, Zhenlin & Li, Chenwei & Tse, Y.K., 2006. "Functional form and spatial dependence in dynamic panels," Economics Letters, Elsevier, vol. 91(1), pages 138-145, April.
    24. Shi, Xiangyu & Xi, Tianyang, 2018. "Race to safety: Political competition, neighborhood effects, and coal mine deaths in China," Journal of Development Economics, Elsevier, vol. 131(C), pages 79-95.
    25. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    26. Yang Yao & Muyang Zhang, 2015. "Subnational leaders and economic growth: evidence from Chinese cities," Journal of Economic Growth, Springer, vol. 20(4), pages 405-436, December.
    27. Yu, Jihai & Zhou, Li-An & Zhu, Guozhong, 2016. "Strategic interaction in political competition: Evidence from spatial effects across Chinese cities," Regional Science and Urban Economics, Elsevier, vol. 57(C), pages 23-37.
    28. Shi, Wei & Lee, Lung-fei, 2017. "Spatial dynamic panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 197(2), pages 323-347.
    29. Lee, Lung-fei & Yu, Jihai, 2010. "A Spatial Dynamic Panel Data Model With Both Time And Individual Fixed Effects," Econometric Theory, Cambridge University Press, vol. 26(2), pages 564-597, April.
    30. 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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    More about this item

    Keywords

    Adjusted quasi score; Dynamic panels; Correlated random effects; Initial-conditions; Martingale difference; Spatial effects; Short panels;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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