Arbeitspapier

Quasi-maximum likelihood and the kernel block bootstrap for nonlinear dynamic models

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 kernel block bootstrap 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.

Sprache
Englisch

Erschienen in
Series: cemmap working paper ; No. CWP60/19

Klassifikation
Wirtschaft
Semiparametric and Nonparametric Methods: General
Statistical Simulation Methods: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Thema
Bootstrap
heteroskedastic and autocorrelation consistent inference
quasi-maximum likelihood estimation

Ereignis
Geistige Schöpfung
(wer)
Parente, Paulo M. D. C.
Smith, Richard J.
Ereignis
Veröffentlichung
(wer)
Centre for Microdata Methods and Practice (cemmap)
(wo)
London
(wann)
2019

DOI
doi:10.1920/wp.cem.2019.6019
Handle
Letzte Aktualisierung
10.03.2025, 11:41 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Parente, Paulo M. D. C.
  • Smith, Richard J.
  • Centre for Microdata Methods and Practice (cemmap)

Entstanden

  • 2019

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