Indirect inference estimation of higher-order spatial autoregressive models. (1st February 2023)

• Record Type: Journal Article
• Authors: Bao, Yong
• Is Part Of: Econometric reviews. Volume 42:Number 3(2023)
• Page Start: 247
• Page End: 280
• Publication Date: 2023-02-01
• Subjects: Heteroskedasticity -- indirect inference -- ordinary least squares -- spatial autoregressive models; C21 -- C31; Econometrics -- Periodicals; 330.015195
• DOI: 10.1080/07474938.2023.2178136
• Languages: English
• ISSNs: 0747-4938

Abstract

Abstract: This paper proposes estimating parameters in higher-order spatial autoregressive models, where the error term also follows a spatial autoregression and its innovations are heteroskedastic, by matching the simple ordinary least squares estimator with its analytical approximate expectation, following the principle of indirect inference. The resulting estimator is shown to be consistent, asymptotically normal, simulation-free, and robust to unknown heteroskedasticity. Monte Carlo simulations demonstrate its good finite-sample properties in comparison with existing estimators. An empirical study of Airbnb rental prices in the city of Asheville illustrates that the structure of spatial correlation and effects of various factors at the early stage of the COVID-19 pandemic are quite different from those during the second summer. Notably, during the pandemic, safety is valued more and on-line reviews are valued much less.