Template-Type: ReDIF-Paper 1.0

Author-Name: Russell Davidson
Author-X-Name-First: Russell
Author-X-Name-Last: Davidson

Author-Name: Andrea Monticini
Author-X-Name-First: Andrea
Author-X-Name-Last: Monticini
Author-Email: andrea.monticini@unicatt.it
Author-Workplace-Name: Università Cattolica del Sacro Cuore
Author-Workplace-Name: Dipartimento di Economia e Finanza, Università Cattolica del Sacro Cuore



Title: Improvements in Bootstrap Inference.


Abstract: 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 formu-
lation 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.
Length: 21
Creation-Date: 2018-04
File-URL: http://dipartimenti.unicatt.it/economia-finanza-def070.pdf
File-Format: Application/pdf
File-Function: First version, 2018
Number: def070
Classification-JEL: C12, C22, C32. 
Keywords: Bootstrap inference, fast double bootstrap, discrete model, conditional fast double bootstrap.
Handle: RePEc:ctc:serie1:def070