时间：2018年10月29日周一下午2:00

地点：云顶12300线路快速检测中心五楼会议室

主讲人：Bao Yong 教授，普渡大学（Purdue University）

题目：Indirect Inference Estimation of Spatial Autoregressions

内容摘要：The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002). The consistency or not of the OLS estimator depends on whether or not 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 is straightforward to implement, does not rely on any distributional assumptions, and is robust to unknown heteroscedasticity. In comparison with the competing generalized method of moments (GMM) estimator proposed by Lin and Lee (2010), the II estimator is found in simulations to have better finite-sample performance and be much less demanding in computational time. For the special case of pure model with no exogenous regressors, the new estimator seems to be the only consistent estimator under heteroscedasticity.