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On the consistency of the LIML estimator of a spatial autoregressive model with many instruments

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  • Liu, Xiaodong

Abstract

This paper derives the LIML estimator for a spatial autoregressive model with endogenous regressors in the presence of many instruments. The LIML estimator is consistent when the number of instruments increases at a slower rate relative to the sample size. Due to spatial correlation, the LIML estimator in general is inconsistent when the number of instruments increases at the same rate as the sample size.

Suggested Citation

  • Liu, Xiaodong, 2012. "On the consistency of the LIML estimator of a spatial autoregressive model with many instruments," Economics Letters, Elsevier, vol. 116(3), pages 472-475.
    • Handle: RePEc:eee:ecolet:v:116:y:2012:i:3:p:472-475
    DOI: 10.1016/j.econlet.2012.04.045
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    References listed on IDEAS

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    1. Donald, Stephen G & Newey, Whitney K, 2001. "Choosing the Number of Instruments," Econometrica, Econometric Society, vol. 69(5), pages 1161-1191, September.
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    4. Lee, Lung-fei, 2007. "The method of elimination and substitution in the GMM estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 140(1), pages 155-189, September.
    5. Liu, Xiaodong & Lee, Lung-fei, 2010. "GMM estimation of social interaction models with centrality," Journal of Econometrics, Elsevier, vol. 159(1), pages 99-115, November.
    6. John C. Chao & Norman R. Swanson, 2005. "Consistent Estimation with a Large Number of Weak Instruments," Econometrica, Econometric Society, vol. 73(5), pages 1673-1692, September.
    7. Hansen, Christian & Hausman, Jerry & Newey, Whitney, 2008. "Estimation With Many Instrumental Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 398-422.
    8. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
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    10. Anderson, T.W. & Kunitomo, Naoto & Matsushita, Yukitoshi, 2010. "On the asymptotic optimality of the LIML estimator with possibly many instruments," Journal of Econometrics, Elsevier, vol. 157(2), pages 191-204, August.
    11. Hasselt, Martijn van, 2010. "Many Instruments Asymptotic Approximations Under Nonnormal Error Distributions," Econometric Theory, Cambridge University Press, vol. 26(2), pages 633-645, April.
    12. 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.
    13. 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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    Cited by:

    1. Júlia Gallego Ziero Uhr & André Luis Squarize Chagas, Daniel de Abreu Pereira Uhr, Renan Porn Peres, 2017. "A study on environmental infractions for Brazilian municipalities: a spatial dynamic panel approach," Working Papers, Department of Economics 2017_13, University of São Paulo (FEA-USP).
    2. Malabika Koley & Anil K. Bera, 2022. "Testing for spatial dependence in a spatial autoregressive (SAR) model in the presence of endogenous regressors," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-46, December.
    3. Liu, Xiaodong & Saraiva, Paulo, 2015. "GMM estimation of SAR models with endogenous regressors," Regional Science and Urban Economics, Elsevier, vol. 55(C), pages 68-79.

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    More about this item

    Keywords

    Limited-information maximum likelihood; Spatial autoregressive models; Many instruments;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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