Document Type

Dissertation

Date of Degree

Spring 2009

Degree Name

PhD (Doctor of Philosophy)

Degree In

Applied Mathematical and Computational Sciences

First Advisor

John Geweke

Abstract

This thesis develops a new Bayesian approach to structural break modeling. The focuses of the approach are the modeling of in-sample structural breaks and forecasting time series allowing out-of-sample breaks. Our model has some desirable features. First, the number of regimes is not fixed and is treated as a random variable in our model. Second, our model adopts a hierarchical prior for regime coefficients, which allows for the regime coefficients of one regime to contain information about regime coefficients of other regimes. However, the regime coefficients can be analytically integrated out of the posterior distribution and therefore we only need to deal with one level of the hierarchy. Third, the implementation of our model is simple and the computational cost is low. Our model is applied to two different time series: S&P 500 monthly returns and U.S. real GDP quarterly growth rates. We linked breaks detected by our model to certain historical events.

Keywords

Markov Chain Monte Carlo, Metropolis-Hastings, Real GDP Growth, S&P 500 Returns, Structural Breaks

Pages

ix, 73 pages

Bibliography

Includes bibliographical references (pages 71-73).

Copyright

Copyright 2009 Yu Jiang

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