Indirect Inference Estimation of Spatial Autoregressions

Yong Bao, Xiaotian Liu and Lihong Yang
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Xiaotian Liu: Department of Economics, Purdue University, West Lafayette, IN 47907, USA
Lihong Yang: School of Economics, Nanjing Audit University, Nanjing 211815, China

Econometrics, 2020, vol. 8, issue 3, 1-26

Abstract: The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significant portion of the total units. This paper presents a unified asymptotic distribution result of the properly recentered OLS estimator and proposes a new estimator that is based on the indirect inference (II) procedure. The resulting estimator can always be used regardless of the degree of aggregate influence on each spatial unit from other units and is consistent and asymptotically normal. The new estimator does not rely on distributional assumptions and is robust to unknown heteroscedasticity. Its good finite-sample performance, in comparison with existing estimators that are also robust to heteroscedasticity, is demonstrated by a Monte Carlo study.

Keywords: spatial autoregression; OLS; indirect inference (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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