JSM 2004 - Toronto

Abstract #301967

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Activity Number: 314
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 9:00 AM to 10:50 AM
Sponsor: Business and Economics Statistics Section
Abstract - #301967
Title: Bayesian Test for Asymmetry and Nonstationarity in MTAR Model with Possibly Incomplete Data
Author(s): Man-Suk Oh*+ and Soo Jung Park and Dong Wan Shin and Byeong Uk Park and Woo Chul Kim
Companies: Ewha Women's University and Ewha Women's University and Ewha Women's University and Seoul National University and Seoul National University
Address: Seo-Dae-Moon Gu, Dae-Hyun Dong 21, Seoul, 120-750, Korea
Keywords: nonlinearity ; model selection ; Markov chain Monte Carlo ; multiple test
Abstract:

We propose an easy and efficient Bayesian test procedure for asymmetry and nonstationarity in MTAR model with possibly incomplete data. Estimation of parameters and missing observations is done by using a Markov chain Monte Carlo (MCMC) method. Testing for asymmetry and nonstationarity is done via test of multiple hypotheses representing various types of symmetry/asymmetry and stationarity/nonstationarity. This allows simultaneous consideration of parameters relevant to asymmetry and nonstationarity of the model, and also enables us to find the sources of asymmetry and nonstationarity when they exist. Posterior probabilities of the hypotheses are easily computed by using MCMC outputs under the full model, with almost no extra cost. We apply the proposed method to a set of Korean unemployment rate data.

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