Quasi-Maximum Likelihood and the Kernel Block Bootstrap for Nonlinear Dynamic Models
Paulo Parente () and
Richard J. Smith
No 2018/59, Working Papers REM from ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa
This paper applies a novel bootstrap method, the kernelblockbootstrap, to quasi-maximum likelihood estimation of dynamic models with stationary strong mixing data. The method rst 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 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.
Keywords: Bootstrap; heteroskedastic and autocorrelation consistent inference; quasi-maximum likelihood estimation. (search for similar items in EconPapers)
JEL-codes: C14 C15 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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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 (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:ise:remwps:wp0592018
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