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Estimating a spatial autoregressive model with autoregressive disturbances based on the indirect inference principle

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  • Yong Bao
  • Xiaotian Liu

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.

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  • Yong Bao & Xiaotian Liu, 2021. "Estimating a spatial autoregressive model with autoregressive disturbances based on the indirect inference principle," Spatial Economic Analysis, Taylor & Francis Journals, vol. 16(4), pages 506-529, October.
    • Handle: RePEc:taf:specan:v:16:y:2021:i:4:p:506-529
    DOI: 10.1080/17421772.2021.1902552
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    Cited by:

    1. Bao, Yong, 2024. "Estimating spatial autoregressions under heteroskedasticity without searching for instruments," Regional Science and Urban Economics, Elsevier, vol. 106(C).

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