Title

Semi-Parametric GMM Estimation of Spatial Autoregressive Models

Publication Type

Journal Article

Publication Date

1-2013

Abstract

We propose semiparametric GMM estimation of semiparametric spatial autoregressive (SAR) models under weak moment conditions. In comparison with the quasi-maximum-likelihood-based semiparametric estimator of Su and Jin (2010), we allow for both heteroscedasticity and spatial dependence in the error terms. We derive the limiting distributions of our estimators for both the parametric and nonparametric components in the model and demonstrate the estimator of the parametric component has the usual -asymptotics. When the error term also follows an SAR process, we propose an estimator for the parameter in the SAR error process and derive the joint asymptotic distribution for both spatial parameters. Consistent estimates for the asymptotic variance-covariance matrices of both the parametric and nonparametric components are provided. Monte Carlo simulations indicate that our estimators perform well in finite samples.

Keywords

Generalized method of moments, Local instruments, Nonlinearity, Semiparametrics, Spatial autoregression

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

167

Issue

2

First Page

543

Last Page

560

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2011.09.034

Publisher

Elsevier

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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