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Dissertation
Inference and prediction in a multiple structural break model of economic time series
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Spring 2009
DOI: 10.17077/etd.k8yeu6p0
Abstract
<p>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.</p>
Details
- Title: Subtitle
- Inference and prediction in a multiple structural break model of economic time series
- Creators
- Yu Jiang - University of Iowa
- Contributors
- John Geweke (Advisor)Kung-Sik Chan (Committee Member)Nathan Savin (Committee Member)Qihe Tang (Committee Member)Lihe Wang (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Applied Mathematical and Computational Sciences
- Date degree season
- Spring 2009
- Publisher
- University of Iowa
- DOI
- 10.17077/etd.k8yeu6p0
- Number of pages
- ix, 73 pages
- Copyright
- Copyright 2009 Yu Jiang
- Language
- English
- Description bibliographic
- Includes bibliographical references (pages 71-73).
- Academic Unit
- Interdisciplinary Graduate Program in Applied Mathematical & Computational Sciences
- Record Identifier
- 9983777289002771
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