Quasi-maximum likelihood and the kernel block bootstrap for nonlinear dynamic models
Paulo Parente () and
Richard J. Smith
Additional contact information
Richard J. Smith: Institute for Fiscal Studies
No CWP60/19, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies
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 rst order asymptotic properties of the kernel block bootstrap method for quasi-maximum likelihood demonstrating, in particular, its consistency and the rst-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.
New Economics Papers: this item is included in nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
https://www.ifs.org.uk/uploads/CW6019-Quasi-maximu ... r-dynamic-models.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found (https://www.ifs.org.uk/uploads/CW6019-Quasi-maximum-likelihood-and-the-kernel-block-bootstrap-for-nonlinear-dynamic-models.pdf [302 Found]--> https://ifs.org.uk/uploads/CW6019-Quasi-maximum-likelihood-and-the-kernel-block-bootstrap-for-nonlinear-dynamic-models.pdf)
Journal Article: Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models (2021)
Working Paper: Quasi-Maximum Likelihood and the Kernel Block Bootstrap for Nonlinear Dynamic Models (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ifs:cemmap:60/19
Ordering information: This working paper can be ordered from
The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE
Access Statistics for this paper
More papers in CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE. Contact information at EDIRC.
Bibliographic data for series maintained by Emma Hyman ().