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.

Sprache
Englisch

Erschienen in
Series: Working Paper ; No. 523

Klassifikation
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
Thema
Bootstrap, Panel data
Panel
Theorie
Bootstrap-Verfahren

Ereignis
Geistige Schöpfung
(wer)
Kapetanios, George
Ereignis
Veröffentlichung
(wer)
Queen Mary University of London, Department of Economics
(wo)
London
(wann)
2004

Handle
Letzte Aktualisierung
20.09.2024, 08:21 MESZ

Datenpartner

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Objekttyp

  • Arbeitspapier

Beteiligte

  • Kapetanios, George
  • Queen Mary University of London, Department of Economics

Entstanden

  • 2004

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