講座題目：Uniform Nonparametric Inference for Time Series
主 讲 人：廖志鹏 University of California, Los Angeles
Zhipeng Liao received his B.A. from Beijing Technology and Business University, M.A. from Peking University and Ph.D. from Yale University. He is an Associate Professor (with tenure) in Economics at UCLA. His research develops methods to use data to select among different economic models, and to make inferences from nonstationary time series data, and to make robust inferences from nonparametric models. His work has been published in the Econometric Theory, the Journal of Econometrics, the Quantitative Economics and the Review of Economic Studies.
This paper provides the first result for the uniform inference based on nonparametric series estimators in a general time-series setting. We develop a strong approximation theory for sample averages of mixingales with dimensions growing with the sample size. We use this result to justify the asymptotic validity of a uniform confidence band for series estimators and show that it can also be used to conduct nonparametric specification test for conditional moment restrictions. New results on the validity of high-dimensional heteroskedasticity and autocorrelation consistent (HAC) estimators are established for making feasible inference. Further extensions include time-series inference theories for intersection bounds and convex sieve M-estimators, which permit applications in partially identified models and nonparametric conditional quantile estimation, respectively. An empirical application on the unemployment volatility puzzle for the search and matching model is provided as an illustration.