Indirect Inference Estimation of Spatial Autoregressions

Econometrics

Xiaotian Liu¹  Yong Bao¹  Lihong Yang² 

[1] Department of Economics, Purdue University, West Lafayette, IN 47907, USA;School of Economics, Nanjing Audit University, Nanjing 211815, China;

关键词: spatial autoregression;    OLS;    indirect inference;   

DOI  :  10.3390/econometrics8030034

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown