Estimating a spatial autoregressive model with autoregressive disturbances based on the indirect inference principle. Issue 4 (2nd October 2021)

• Record Type: Journal Article
• Authors: Bao, Yong; Liu, Xiaotian
• Is Part Of: Spatial economic analysis. Volume 16:Issue 4(2021)
• Page Start: 506
• Page End: 529
• Publication Date: 2021-10-02
• Subjects: spatial autoregressive model -- indirect inference -- ordinary least squares; C21 -- C31; Space in economics -- Periodicals; Economic geography -- Periodicals; Space in economics -- Econometric models -- Periodicals; Urbanization -- Periodicals; Espace (Économie politique) -- Périodiques; Économétrie -- Périodiques; Géographie économique -- Périodiques; Économie industrielle -- Périodiques; Économie du travail -- Périodiques; Économie urbaine -- Périodiques; Politique économique -- Périodiques; Relations économiques internationales -- Périodiques; 330.9
• DOI: 10.1080/17421772.2021.1902552
• Languages: English
• ISSNs: 1742-1772

Abstract

ABSTRACT: This paper proposes a new estimation procedure for the first-order spatial autoregressive (SAR) model, where the disturbance term also follows a first-order autoregression and its innovations may be heteroscedastic. The estimation procedure is based on the principle of indirect inference that matches the ordinary least squares estimator of the two SAR coefficients (one in the outcome equation and the other in the disturbance equation) with its approximate analytical expectation. The resulting estimator is shown to be consistent, asymptotically normal and robust to unknown heteroscedasticity. Monte Carlo experiments are provided to show its finite-sample performance in comparison with existing estimators that are based on the generalized method of moments. The new estimation procedure is applied to empirical studies on teenage pregnancy rates and Airbnb accommodation prices.