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
First Committee Member
Kung-Sik Chan
Second Committee Member
Nathan Savin
Third Committee Member
Qihe Tang
Fourth Committee Member
Lihe Wang
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