讲座主题：Indirect Inference Estimation of Spatial Autoregressions
内容简介：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.
个人简介：鲍勇，美国普渡大学经济学院教授。主要研究领域：计量经济学理论与应用。先后在《Journal of Econometrics》、《Journalof Business & Economic Statistics》、《Journal of Multivariate Analysis》等杂志公开发表论文30余篇。