Seminar: Quasi-Maximum Likelihood and The Kernel Block Bootstrap for Nonlinear Dynamic Models – School of Economics Seminar: Quasi-Maximum Likelihood and The Kernel Block Bootstrap for Nonlinear Dynamic Models – School of Economics

Seminar: Quasi-Maximum Likelihood and The Kernel Block Bootstrap for Nonlinear Dynamic Models

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.

Date

Mar 21 2019
Expired!

Time

11:00 am - 12:30 pm
Category

Organizer

Dave Mc Manamon
Phone
93514587
Email
dave.mcmanamon@sydney.edu.au

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