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GMM estimation of spatial autoregressive models with unknown heteroskedasticity

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  • Lin, Xu
  • Lee, Lung-fei

Abstract

In the presence of heteroskedastic disturbances, the MLE for the SAR models without taking into account the heteroskedasticity is generally inconsistent. The 2SLS estimates can have large variances and biases for cases where regressors do not have strong effects. In contrast, GMM estimators obtained from certain moment conditions can be robust. Asymptotically valid inferences can be drawn with consistently estimated covariance matrices. Efficiency can be improved by constructing the optimal weighted estimation. The approaches are applied to the study of county teenage pregnancy rates. The empirical results show a strong spatial convergence among county teenage pregnancy rates.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 157 (2010)
Issue (Month): 1 (July)
Pages: 34-52

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Handle: RePEc:eee:econom:v:157:y:2010:i:1:p:34-52

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Web page: http://www.elsevier.com/locate/jeconom

Related research

Keywords: Spatial autoregression Unknown heteroskedasticity Robustness Consistent covariance matrix GMM;

References

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  12. 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.
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Citations

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Cited by:
  1. Suárez Cano, Patricia & Mayor Fernández, Matías & Cueto Iglesias, Begoña, 2011. "How important is access to employment offices in Spain? An urban and non-urban perspective," Investigaciones Regionales, Asociación Española de Ciencia Regional, issue 21, pages 119-140.
  2. Matthias Arnold & Sebastian Stahlberg & Dominik Wied, 2013. "Modeling different kinds of spatial dependence in stock returns," Empirical Economics, Springer, vol. 44(2), pages 761-774, April.
  3. Badi H. Baltagi & Zhenlin Yang, 2013. "Heteroskedasticity and Non-normality Robust LM Tests for Spatial Dependence," Center for Policy Research Working Papers 156, Center for Policy Research, Maxwell School, Syracuse University.
  4. Moscone, F. & Tosetti, E., 2011. "GMM estimation of spatial panels with fixed effects and unknown heteroskedasticity," Regional Science and Urban Economics, Elsevier, vol. 41(5), pages 487-497, September.
  5. Doğan, Osman & Taşpınar, Süleyman, 2013. "GMM estimation of spatial autoregressive models with moving average disturbances," Regional Science and Urban Economics, Elsevier, vol. 43(6), pages 903-926.
  6. Doğan, Osman & Taşpınar, Süleyman, 2014. "Spatial autoregressive models with unknown heteroskedasticity: A comparison of Bayesian and robust GMM approach," Regional Science and Urban Economics, Elsevier, vol. 45(C), pages 1-21.
  7. Su, Liangjun, 2012. "Semiparametric GMM estimation of spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 167(2), pages 543-560.
  8. Liu, Xiaodong & Lee, Lung-fei & Bollinger, Christopher R., 2010. "An efficient GMM estimator of spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 159(2), pages 303-319, December.
  9. Jin, Fei & Lee, Lung-fei, 2012. "Approximated likelihood and root estimators for spatial interaction in spatial autoregressive models," Regional Science and Urban Economics, Elsevier, vol. 42(3), pages 446-458.
  10. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2014. "A Two Stage Approach to Spatiotemporal Analysis with Strong and Weak Cross-Sectional Dependence," CESifo Working Paper Series 4592, CESifo Group Munich.
  11. Jin, Fei & Lee, Lung-fei, 2013. "Cox-type tests for competing spatial autoregressive models with spatial autoregressive disturbances," Regional Science and Urban Economics, Elsevier, vol. 43(4), pages 590-616.
  12. Nicolas Debarsy & Fei Jin & Lung-Fei Lee, 2013. "Large sample properties of the matrix exponential spatial specification with an application to FDI," Working Papers hal-00858174, HAL.
  13. 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.
  14. Zhenlin Yang, 2013. "LM Tests of Spatial Dependence Based on Bootstrap Critical Values," Working Papers 03-2013, Singapore Management University, School of Economics.
  15. Bai, Jushan & Li, Kunpeng, 2013. "Spatial panel data models with common shocks," MPRA Paper 52786, University Library of Munich, Germany, revised 09 Mar 2014.
  16. Harald Badinger & Peter Egger, 2014. "Fixed Effects and Random Effects Estimation of Higher-Order Spatial Autoregressive Models with Spatial Autoregressive and Heteroskedastic Disturbances," CESifo Working Paper Series 4847, CESifo Group Munich.
  17. 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.
  18. Han, Xiaoyi & Lee, Lung-fei, 2013. "Model selection using J-test for the spatial autoregressive model vs. the matrix exponential spatial model," Regional Science and Urban Economics, Elsevier, vol. 43(2), pages 250-271.
  19. Osman Dogan, 2013. "Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with Moving Average Disturbance Term," Working Papers 002, City University of New York Graduate Center, Ph.D. Program in Economics.
  20. Takahisa Yokoi, 2011. "Efficient Maximum Likelihood Estimation of Spatial Autoregressive Models with Normal but Heteroskedastic Disturbances," ERSA conference papers ersa10p536, European Regional Science Association.
  21. Su, Liangjun & Jin, Sainan, 2010. "Profile quasi-maximum likelihood estimation of partially linear spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 157(1), pages 18-33, July.
  22. Osman Dogan & Suleyman Taspinar, 2013. "GMM Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances," Working Papers 001, City University of New York Graduate Center, Ph.D. Program in Economics.

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