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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.
Authors
Paulo Parente
Richard J. Smith
Working Paper details
- DOI
- 10.1920/wp.cem.2019.6019
- Publisher
- The IFS
Suggested citation
Parente, P and Smith, R. (2019). Quasi-maximum likelihood and the kernel block bootstrap for nonlinear dynamic models. London: The IFS. Available at: https://ifs.org.uk/publications/quasi-maximum-likelihood-and-kernel-block-bootstrap-nonlinear-dynamic-models (accessed: 19 April 2024).
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