Statistical methods for dynamic panel data and their applications
Abstract/Contents
- Abstract
- Dynamic panel data models are widely studied in econometrics and in biostatistics. We propose a unified approach based on the empirical Bayes methodology to provide a computationally efficient framework for the analysis and prediction of the dynamic panel data with many individuals. In the econometrics literature, an alternative approach to dynamic panel data is to use moment restrictions. How to choose the combination of model and moment restrictions that yields the proper balance between bias and variance of the GMM or the GEL estimator is a fundamental problem in moment restriction models. We propose a new two-stage model and moment selection procedure and derive new asymptotic properties of maximum empirical likelihood estimators. We apply our proposed methods to simulation studies and empirical analysis of dynamic panel data. Related applications include modeling joint default probabilities of multiple firms and empirical study of subprime mortgage loans.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2013 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Su, Yong |
|---|---|
| Associated with | Stanford University, Department of Statistics. |
| Primary advisor | Lai, T. L |
| Thesis advisor | Lai, T. L |
| Thesis advisor | Rajaratnam, Balakanapathy |
| Thesis advisor | Walther, Guenther |
| Advisor | Rajaratnam, Balakanapathy |
| Advisor | Walther, Guenther |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Yong Su. |
|---|---|
| Note | Submitted to the Department of Statistics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2013. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2013 by Yong Su
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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