报告题目：Learning Before Testing: A Selective Nonparametric Test for Conditional Moment
Restrictions
报 告 人：廖志鹏 加州大学洛杉矶分校
报告时间：2023年6月20日(周二)，16:30-18:00
报告地点：中科院数学与系统科学研究院南楼N204
腾讯会议ID：929 2139 6317
内容摘要
We develop a new test for conditional moment restrictions via nonparametric series regression, with approximating functions selected by Lasso. A key novelty of our approach is to account for the effect of the data-driven selection, yielding a new critical value constructed on the basis of a nonstandard truncated-Gaussian asymptotic approximation. We show that the test is correctly sized and attains a well-defined sense of adaptiveness that generally results in better power than existing methods. The improvement afforded by the new test is demonstrated in a Monte Carlo study and an empirical application on the conditional evaluation of inflation forecasts.
主讲人简介
廖志鹏，耶鲁大学经济学博士，美国加州大学洛杉矶分校 (UCLA) 经济学终身教授，主要研究领域为理论计量经济学和应用计量经济学。曾在Econometrica、Review of Economic Studies、Annals of Statistics、Journal of Econometrics、Quantitative Economics等经济学、统计学国际顶级期刊上发表学术论文数十篇，并长期担任Econometric Theory、Journal of Business & Economic Statistics、Econometrics Journal、Econometric Reviews等经济学国际顶尖学术期刊的副主编。
