Arbeitspapier
A bootstrap procedure for panel datasets with many cross-sectional units
This paper considers the issue of bootstrap resampling in panel datasets. The availability of datasets with large temporal and cross sectional dimensions suggests the possibility of new resampling schemes. We suggest one possibility which has not been widely explored in the literature. It amounts to constructing bootstrap samples by resampling whole cross sectional units with replacement. In cases where the data do not exhibit cross sectional dependence but exhibit temporal dependence, such a resampling scheme is of great interest as it allows the application of i.i.d. bootstrap resampling rather than block bootstrap resampling. It is well known that the former enables superior approximation to distributions of statistics compared to the latter. We prove that the bootstrap based on cross sectional resampling provides asymptotic refinements. A Monte Carlo study illustrates the superior properties of the new resampling scheme compared to the block bootstrap.
- Language
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Englisch
- Bibliographic citation
-
Series: Working Paper ; No. 523
- Classification
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Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
- Subject
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Bootstrap, Panel data
Panel
Theorie
Bootstrap-Verfahren
- Event
-
Geistige Schöpfung
- (who)
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Kapetanios, George
- Event
-
Veröffentlichung
- (who)
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Queen Mary University of London, Department of Economics
- (where)
-
London
- (when)
-
2004
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Kapetanios, George
- Queen Mary University of London, Department of Economics
Time of origin
- 2004
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