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X-ORIGINAL-URL:https://economics-events.sydney.edu.au/
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UID:MEC-9431c87f273e507e6040fcb07dcb4509@economics-events.sydney.edu.au
DTSTART:20190321T000000Z
DTEND:20190321T013000Z
DTSTAMP:20190312T041200Z
CREATED:20190312
LAST-MODIFIED:20190315
SUMMARY:Seminar: Quasi-Maximum Likelihood and The Kernel Block Bootstrap for Nonlinear Dynamic Models
DESCRIPTION:The School of Economics would like to invite you to a seminar by:\nRichard J. Smith\nUniversity of Cambridge\nCo-author:\nPaulo M.D.C. Parente (ISEG- Lisbon School of Economics & Management)\nThis paper applies a novel bootstrap method, the kernel block bootstrap, to quasi-maximum likelihood estimation of dynamic models with stationary strong mixing data. The method first kernel weights the components comprising the quasi-log likelihood function in an appropriate way and then samples the resultant transformed components using the standard “m out of n” bootstrap. We investigate the first order asymptotic properties of the KBB method for quasi-maximum likelihood demonstrating, in particular, its consistency and the first-order asymptotic validity of the bootstrap approximation to the distribution of the quasi-maximum likelihood estimator. A set of simulation experiments for the mean regression model illustrates the efficacy of the kernel block bootstrap for quasi-maximum likelihood estimation.\n
X-ALT-DESC;FMTTYPE=text/html:The School of Economics would like to invite you to a seminar by:

**Richard J. Smith**

University of Cambridge

Co-author:

Paulo M.D.C. Parente (ISEG- Lisbon School of Economics & Management)

This paper applies a novel bootstrap method, the kernel block bootstrap, to quasi-maximum likelihood estimation of dynamic models with stationary strong mixing data. The method first kernel weights the components comprising the quasi-log likelihood function in an appropriate way and then samples the resultant transformed components using the standard “m out of n” bootstrap. We investigate the first order asymptotic properties of the KBB method for quasi-maximum likelihood demonstrating, in particular, its consistency and the first-order asymptotic validity of the bootstrap approximation to the distribution of the quasi-maximum likelihood estimator. A set of simulation experiments for the mean regression model illustrates the efficacy of the kernel block bootstrap for quasi-maximum likelihood estimation.

URL:https://economics-events.sydney.edu.au/calendar/seminar-quasi-maximum-likelihood-and-the-kernel-block-bootstrap-for-nonlinear-dynamic-models/
ORGANIZER;CN=Dave Mc Manamon:MAILTO:dave.mcmanamon@sydney.edu.au
CATEGORIES:Seminars
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