Wild bootstrap approach for testing slope homogeneity in panel data models
Various bootstrap methods can be used for econometric analysis. In certain circumstances, appropriately chosen bootstrap method generally work very well for regression models with independent and identically distributed error terms, and the regressors and error terms are strictly exogenous. However,...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/77123 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Various bootstrap methods can be used for econometric analysis. In certain circumstances, appropriately chosen bootstrap method generally work very well for regression models with independent and identically distributed error terms, and the regressors and error terms are strictly exogenous. However, there exist some common case, where the pooled regression models has dependent error, thus normal bootstrap method does not always perform well. This paper considers hypothesis testing for slope homogeneity in panel data models with heteroscedastic disturbances using wild bootstrap approach. Simulation experiments show that using wild bootstrap to approximate the distribution of each test statistic performs well in this context. |
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