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GMM estimation of spatial autoregressive models with moving average disturbances

  • Doğan, Osman
  • Taşpınar, Süleyman

In this paper, we introduce the one-step generalized method of moments (GMM) estimation methods considered in Lee (2007a) and Liu, Lee, and Bollinger (2010) to spatial models that impose a spatial moving average process for the disturbance term. First, we determine the set of best linear and quadratic moment functions for GMM estimation. Second, we show that the optimal GMM estimator (GMME) formulated from this set is the most efficient estimator within the class of GMMEs formulated from the set of linear and quadratic moment functions. Our analytical results show that the one-step GMME can be more efficient than the quasi maximum likelihood (QMLE), when the disturbance term is simply i.i.d. With an extensive Monte Carlo study, we compare its finite sample properties against the MLE, the QMLE and the estimators suggested in Fingleton (2008a).

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Article provided by Elsevier in its journal Regional Science and Urban Economics.

Volume (Year): 43 (2013)
Issue (Month): 6 ()
Pages: 903-926

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Handle: RePEc:eee:regeco:v:43:y:2013:i:6:p:903-926
Contact details of provider: Web page: http://www.elsevier.com/locate/regec

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  6. Osman Dogan & Suleyman Taspinar, 2013. "GMM Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances," Working Papers 1, City University of New York Graduate Center, Ph.D. Program in Economics.
  7. Osman Dogan, 2013. "Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with Moving Average Disturbance Term," Working Papers 2, City University of New York Graduate Center, Ph.D. Program in Economics.
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  17. Bernard Fingleton, 2008. "A Generalized Method of Moments Estimator for a Spatial Panel Model with an Endogenous Spatial Lag and Spatial Moving Average Errors," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(1), pages 27-44.
  18. 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.
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