JSM 2004 Online Program

Abstract #300217

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Activity Number:	4
Type:	Invited
Date/Time:	Sunday, August 8, 2004 : 2:00 PM to 3:50 PM
Sponsor:	Business and Economics Statistics Section
Abstract - #300217
Title:	Semiparametric Markov Bootstrap
Author(s):	Bruce E. Hansen*+
Companies:	University of Wisconsin
Address:	Dept. of Economics, Madison, WI, 53706,
Keywords:	time series ; kernel estimation ; nonparametric ; conditional distribution
Abstract:	The time-series Markov bootstrap is based on a nonparametric estimate of the one-step-ahead conditional distribution. When the conditioning set is moderately large, nonparametric estimation is not feasible. We propose a semiparametric specification of the conditional distribution. A parametric reduces the series to one with reduced serial dependence, and then the remainder is treated nonparametrically. The reduction in the nonparametric dimension improves the rate of convergence of the estimator, and therefore improves the refinement rate of the bootstrap.

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