Long-range dependent common factor models: A Bayesian approach

Authors

Nan Jung Hsu, National Tsing Hua University
Bonnie K. Ray, New Jersey Institute of Technology
F. Jay Breidt, Colorado State University

Document Type

Article

Publication Date

1-1-2001

Abstract

We propose a simulation-based Bayesian approach to analyze multivariate time series with possible common long-range dependent factors. A state-space approach is used to represent the likelihood function in a tractable manner. The approach taken here allows for extension to fit a non-Gaussian multivariate stochastic volatility (MVSV) model with common long-range dependent components. The method is illustrated for a set of stock returns for companies having similar annual sales. Copyright © 2001 by Marcel Dekker, Inc.

Identifier

0034863001 (Scopus)

Publication Title

Communications in Statistics Theory and Methods

External Full Text Location

https://doi.org/10.1081/STA-100104349

ISSN

03610926

First Page

1047

Last Page

1061

Issue

6

Volume

30

Grant

-9623884

Fund Ref

National Science Foundation

Recommended Citation

Hsu, Nan Jung; Ray, Bonnie K.; and Breidt, F. Jay, "Long-range dependent common factor models: A Bayesian approach" (2001). Faculty Publications. 15355.
https://digitalcommons.njit.edu/fac_pubs/15355