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Estimation of spatial panel data models with randomly missing data in the dependent variable

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  • Wang, Wei
  • Lee, Lung-fei

Abstract

We suggest and compare different methods for estimating spatial autoregressive panel models with randomly missing data in the dependent variable. We start with a random effects model and then generalize the model by introducing the spatial Mundlak approach. A nonlinear least squares method is suggested and a generalized method of moments estimation is developed for the model. A two-stage least squares estimation with imputation is proposed as well. We analytically compare these estimation methods and find that the generalized nonlinear least squares, best generalized two-stage least squares with imputation, and best method of moments estimators have identical asymptotic variances. The robustness of these estimation methods against unknown heteroscedasticity is also stressed since the traditional maximum likelihood approach yields inconsistent estimates under unknown heteroscedasticity. We provide finite sample evidence through Monte Carlo experiments.

Suggested Citation

  • Wang, Wei & Lee, Lung-fei, 2013. "Estimation of spatial panel data models with randomly missing data in the dependent variable," Regional Science and Urban Economics, Elsevier, vol. 43(3), pages 521-538.
  • Handle: RePEc:eee:regeco:v:43:y:2013:i:3:p:521-538
    DOI: 10.1016/j.regsciurbeco.2013.02.001
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    References listed on IDEAS

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    1. James P. LeSage & R. Kelley Pace, 2004. "Models for Spatially Dependent Missing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 233-254, September.
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    3. 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.
    4. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    5. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    6. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464.
    7. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
    8. Irani Arraiz & David M. Drukker & Harry H. Kelejian & Ingmar R. Prucha, 2010. "A Spatial Cliff-Ord-Type Model With Heteroskedastic Innovations: Small And Large Sample Results," Journal of Regional Science, Wiley Blackwell, vol. 50(2), pages 592-614.
    9. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    10. H. Kelejian, Harry & Prucha, Ingmar R., 2001. "On the asymptotic distribution of the Moran I test statistic with applications," Journal of Econometrics, Elsevier, vol. 104(2), pages 219-257, September.
    11. Lin, Xu & Lee, Lung-fei, 2010. "GMM estimation of spatial autoregressive models with unknown heteroskedasticity," Journal of Econometrics, Elsevier, vol. 157(1), pages 34-52, July.
    12. Harry Kelejian & Ingmar Prucha, 2010. "Spatial models with spatially lagged dependent variables and incomplete data," Journal of Geographical Systems, Springer, vol. 12(3), pages 241-257, September.
    13. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    14. Wei Wang & Lung‐Fei Lee, 2013. "Estimation of spatial autoregressive models with randomly missing data in the dependent variable," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 73-102, February.
    15. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    16. Lung-fei Lee & Jihai Yu, 2012. "QML Estimation of Spatial Dynamic Panel Data Models with Time Varying Spatial Weights Matrices," Spatial Economic Analysis, Taylor & Francis Journals, vol. 7(1), pages 31-74, March.
    17. Lee, Lung-fei, 2007. "GMM and 2SLS estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 137(2), pages 489-514, April.
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    Citations

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    Cited by:

    1. Ronald Davies & Helen Naughton, 2014. "Cooperation in environmental policy: a spatial approach," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 21(5), pages 923-954, October.
    2. Eleonora Patacchini & Xiaodong Liu & Edoardo Rainone, 2013. "The Allocation of Time in Sleep: A Social Network Model with Sampled Data," Center for Policy Research Working Papers 162, Center for Policy Research, Maxwell School, Syracuse University.
    3. König, Michael & Liu, Xiaodong & Zenou, Yves, 2014. "R&D Networks: Theory, Empirics and Policy Implications," CEPR Discussion Papers 9872, C.E.P.R. Discussion Papers.
    4. 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.
    5. Ronald B. Davies & Stephan Klasen, 2013. "Of Donor Coordination, Free-Riding, Darlings, and Orphans: The Dependence of Bilateral Aid on Other Bilateral Giving," CESifo Working Paper Series 4177, CESifo Group Munich.
    6. Yun, Seong Do & Gramig, Benjamin M & Delgado, Michael S. & Florax, Raymond J.G.M., 2015. "Does Spatial Correlation Matter in Econometric Models of Crop Yield Response and Weather?," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205465, Agricultural and Applied Economics Association;Western Agricultural Economics Association.
    7. Menegaki, Angeliki N., 2013. "Accounting for unobserved management in renewable energy & growth," Energy, Elsevier, vol. 63(C), pages 345-355.
    8. Firgo, Matthias & Pennerstorfer, Dieter & Weiss, Christoph R., 2015. "Centrality and pricing in spatially differentiated markets: The case of gasoline," International Journal of Industrial Organization, Elsevier, vol. 40(C), pages 81-90.
    9. repec:eee:csdana:v:120:y:2018:i:c:p:98-110 is not listed on IDEAS

    More about this item

    Keywords

    Spatial autoregressive models; Missing data; Dependent variable; GMM estimation; Nonlinear least squares; Imputation; Mundlak approach; Unknown heteroscedasticity;

    JEL classification:

    • 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
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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