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Indirect Inference Estimation of Spatial Autoregressions

by 1,*, 1 and 2,3
Department of Economics, Purdue University, West Lafayette, IN 47907, USA
School of Economics, Nanjing Audit University, Nanjing 211815, China
National Academy of Development and Strategy, Renmin University of China, Beijing 100872, China
Author to whom correspondence should be addressed.
Econometrics 2020, 8(3), 34;
Received: 1 May 2020 / Revised: 5 August 2020 / Accepted: 31 August 2020 / Published: 3 September 2020
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. View Full-Text
Keywords: spatial autoregression; OLS; indirect inference spatial autoregression; OLS; indirect inference
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MDPI and ACS Style

Bao, Y.; Liu, X.; Yang, L. Indirect Inference Estimation of Spatial Autoregressions. Econometrics 2020, 8, 34.

AMA Style

Bao Y, Liu X, Yang L. Indirect Inference Estimation of Spatial Autoregressions. Econometrics. 2020; 8(3):34.

Chicago/Turabian Style

Bao, Yong, Xiaotian Liu, and Lihong Yang. 2020. "Indirect Inference Estimation of Spatial Autoregressions" Econometrics 8, no. 3: 34.

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