Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/77123
Title: Wild bootstrap approach for testing slope homogeneity in panel data models
Authors: Chong, Shun Jie
Keywords: DRNTU::Science::Mathematics::Statistics
Issue Date: 2019
Abstract: 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.
URI: http://hdl.handle.net/10356/77123
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Student Reports (FYP/IA/PA/PI)

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