Publication Type
Journal Article
Version
publishedVersion
Publication Date
4-2012
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
Citation
SU, Liangjun.
Semiparametric GMM Estimation of Spatial Autoregressive Models. (2012). Journal of Econometrics. 167, (2), 543-560.
Available at: https://ink.library.smu.edu.sg/soe_research/1423
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.jeconom.2011.09.034