Projects per year
Abstract
In this article, we propose a simple inferential method for a wide class of panel data models with a focus on such cases that have both serial correlation and cross-sectional dependence. In order to establish an asymptotic theory to support the inferential method, we develop some new and useful higher-order expansions, such as Berry-Esseen bound and Edgeworth Expansion, under a set of simple and general conditions. We further demonstrate the usefulness of these theoretical results by explicitly investigating a panel data model with interactive effects which nests many traditional panel data models as special cases. Finally, we show the superiority of our approach over several natural competitors using extensive numerical studies. Supplementary materials for this article are available online.
Original language | English |
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Number of pages | 12 |
Journal | Journal of the American Statistical Association |
DOIs | |
Publication status | Accepted/In press - 2023 |
Keywords
- Dependent wild bootstrap
- Edgeworth expansion
- Fund performance evaluation
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New Insights on Modelling Time Trends with Panel Data: Theory and Practice
Peng, B., Yao, W. & Westerlund, J.
1/01/21 → 19/12/25
Project: Research
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New methods for modelling complex trends in climate and energy time series
Anderson, H., Gao, J., Vahid-Araghi, F., Wei, W., Phillips, P. C. B., Linton, O. B. & Lunde, A.
3/08/20 → 31/12/24
Project: Research