Online Program Home
My Program

Abstract Details

Activity Number: 480
Type: Topic Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #319631 View Presentation
Title: Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model
Author(s): Xinyi Xu* and Zhiguang Xu and Steven N. MacEachern
Companies: The Ohio State University and Chase and The Ohio State University
Keywords: Autoregressive process ; Copula model ; GARCH ; probability integral transformation

We propose a class of nonparametric Bayesian models for analyzing stationary time series data using a copula approach, which retains many desirable properties of classic time series models while allowing the innovation distributions to be non-Gaussian. Our models separate estimating the marginal (limiting) distribution of a time series from modeling the internal dynamics of the series. They provide coherent adjustments of time scales and are compatible with many extensions, including changes in volatility of the series. We describe basic properties of the models, show their ability to recover non-Gaussian marginal distributions, and use a GARCH modification of the basic model to analyze stock index return series. The models are found to provide better fit and improved short-range and long-range predictions than Gaussian competitors. They are extensible to a large variety of fields, including continuous time models, spatial models, models for multiple series, models driven by external covariate streams, and non-stationary models.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association