An efficient GMM estimator of spatial autoregressive models

Xiaodong Liu, Lung Fei Lee, Christopher R. Bollinger

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

In this paper, we consider GMM estimation of the regression and MRSAR models with SAR disturbances. We derive the best GMM estimator within the class of GMM estimators based on linear and quadratic moment conditions. The best GMM estimator has the merit of computational simplicity and asymptotic efficiency. It is asymptotically as efficient as the ML estimator under normality and asymptotically more efficient than the Gaussian QML estimator otherwise. Monte Carlo studies show that, with moderate-sized samples, the best GMM estimator has its biggest advantage when the disturbances are asymmetrically distributed. When the diagonal elements of the spatial weights matrix have enough variation, incorporating kurtosis of the disturbances in the moment functions will also be helpful.

Original languageEnglish
Pages (from-to)303-319
Number of pages17
JournalJournal of Econometrics
Volume159
Issue number2
DOIs
StatePublished - Dec 2010

Keywords

  • Efficiency
  • GMM
  • QMLE
  • Spatial autoregressive models
  • Spatial correlated disturbances

ASJC Scopus subject areas

  • Economics and Econometrics

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