一、报告题目：

Indirect Inference Estimation of Spatial Autoregressions

二、报告人：

Yong Bao，普渡大学

三、报告时间：

2019年6月10日 （周一） 下午 14:00-15:30

四、报告地点：

知新楼B423

五、报告人简介：

Yong Bao is a Professor of Economics at the Krannert School of Management at Purdue University (West Lafayette, IN, USA). Professor Bao's research and teaching interests are in econometrics, including finite-sample theory, time series, and financial econometrics. He has published in the Journal of Econometrics, Econometric Theory, Econometrics Reviews, among others.

六、 报告摘要：

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 unied 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 in fluence 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. 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. The II procedure is applied to an empirical study of teenage pregnancy rates in 10 states in the U.S. and it corroborates Lin and Lee's (2010) finnding that gives strong evidence of spatial correlation among teenage pregnancy rates.

七、主办单位：

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