Functional Quantile Autoregression

Speaker：Zhijie Xiao（Professor, Boston College）

Host：Zhu Chuanqi , Associate Professor, Lingnan College

Time and Date：14:30, Jun. 30, 2023

Venue: Wang Daohan Conference Room(101), Lingnan Hall

Language: English + Chinese

Abstract:

This paper proposes a new class of time series models, the functional quantile autoregression (FQAR) models, in which the conditional distribution of the observation at the current time point is affected by its past distributional information, and expressed as a functional of the past conditional quantile functions. The model can capture systematic influences of the past distributional information on the current distribution, and therefore constitute a significant extension of traditional time series models in which the effect of conditioning information is confined to only a few selected characteristics of the past distribution. We propose a sieve estimator for the model. The asymptotic properties and finite sample performance of this model are investigated.

Profile of the speaker:

Education：

Ph.D., Economics, 1997, Yale University

M. Ph., Economics, 1996, Yale University

M. A., Economics, 1995, Yale University

M. Sc., Mathematics and Economics, 1991, Renmin (People’s) University of China

B. Sc., Mathematics and Computer Science, 1988, Renmin (People’s) University of China