讲座题目：Quantile Factor Models

　　报告人： 陈亮 助理教授(上海财经大学)

　　报告时间：2019年5月16日(周四)上午10点00分

　　报告地点：经管院A204

　　主办单位：数理经济与数理金融系

　　主持人：庄额嘉

　　摘要:

　　Quantile Factor Models (QFM) represent a new class of factor models for high-dimensional panel data. Unlike Approximate Factor Models (AFM), where only mean-shifting factors can be extracted, QFM also allow to recover unobserved factors shifting other relevant parts of the distributions of observed variables. A quantile regression approach, labeled Quantile Factor Analysis (QFA), is proposed to consistently estimate all the quantile-dependent factors and loadings. Their asymptotic distribution is then derived using a kernel-smoothed version of the QFA estimators. Two consistent model selection criteria, based on information criteria and rank minimization, are developed to determine the number of factors at eachquantile. Moreover, in contrast to the conditions required for the use of Principal Components Analysis in AFM, QFA estimation remains valid even when the idiosyncratic errors have heavy-tailed distributions. Three empirical applications (regarding climate, financial and macroeconomic panel data) provide evidence that extra factors shifting quantiles other than the means could be relevant in practice.

　　简介 ：

　　陈亮, 上海财经大学助理教授, 2013 年毕业于 Universidad Carlos III de Madrid, 之后在 University of Oxford 担任博士于研究员至 2016 年, 文章发表于 Journal of Econometrics 與 Economic Letters (2篇), 并有一篇文章在 Econometrica R&R (第二轮)。