演講者簡介 : Prof. Yanqin Fan為Ph.D. in Economics，University of Western Ontario（1990）。
現為Professor, Department of Economics, Vanderbilt University。
其主要研究領域為Nonparametric Statistics及Econometrics。
演講摘要 : In this paper, we establish asymptotic properties, including the consistency and asymptotic normality, of nonparametric estimators of the sharp bounds on the correlation between two random variables. We demonstrate both theoretically and numerically that the sharp bounds may differ from the traditionally used bounds [-1,1] and the nonparametric estimators of the sharp bounds shed light on the strength of the type of dependence, linear or nonlinear, between two random variables. To facilitate inference on the true sharp bounds, we provide easy-to-compute estimators of the asymptotic variances of the nonparametric estimators of the sharp bounds. Using the sharp correlation bounds on the unobserved covariates, we derive sharp bounds on the correlation of durations in bivariate hazard rate models with unobserved heterogeneity and the correlation of dependent variables in bivariate log-linear regression models with unobserved covariates. These results provide insight on the selection of distributions of the unobserved heterogeneity in bivariate hazard rate models and unobserved covariates in log-linear regression models.
