Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/211153
Authors: 
Parente, Paulo M. D. C.
Smith, Richard J.
Year of Publication: 
2019
Series/Report no.: 
cemmap working paper No. CWP60/19
Abstract: 
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.
Subjects: 
Bootstrap
heteroskedastic and autocorrelation consistent inference
quasi-maximum likelihood estimation
JEL: 
C14
C15
C22
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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