Arbeitspapier
Improvements in bootstrap inference
The fast double bootstrap can improve considerably on the single bootstrap when the bootstrapped statistic is approximately independent of the bootstrap DGP. This is because, among the approximations that underlie the fast double bootstrap (FDB), is the assumption of such independence. In this paper, use is made of a discrete formulation of bootstrapping in order to develop a conditional version of the FDB, which makes use of the joint distribution of a statistic and its bootstrap counterpart, rather than the joint distribution of the statistic and the full distribution of its bootstrap counterpart, which is available only by means of a simulation as costly as the full double bootstrap. Simulation evidence shows that the conditional FDB can greatly improve on the performance of the FDB when the statistic and the bootstrap DGP are far from independent, while giving similar results in cases of near independence.
- Sprache
-
Englisch
- Erschienen in
-
Series: Working Paper ; No. 70
- Klassifikation
-
Wirtschaft
Hypothesis Testing: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- Thema
-
Bootstrap inference
fast double bootstrap
discrete model
conditional fast double bootstrap
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Davidson, Russell
Monticini, Andrea
- Ereignis
-
Veröffentlichung
- (wer)
-
Università Cattolica del Sacro Cuore, Dipartimento di Economia e Finanza (DISCE)
- (wo)
-
Milano
- (wann)
-
2018
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Davidson, Russell
- Monticini, Andrea
- Università Cattolica del Sacro Cuore, Dipartimento di Economia e Finanza (DISCE)
Entstanden
- 2018
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