Finite-sample properties of the bootstrap estimator in a Markov-switching model

T. W. Ho

doi:10.1080/02664760120074924

Finite-sample properties of the bootstrap estimator in a Markov-switching model

T. W. Ho^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

The size distortion problem is clearly indicative of the small-sample approximation in the Markov-switching regression model. This paper shows that the bootstrap procedure can relieve the effects that this problem has. Our Monte Carlo simulation results reveal that the bootstrap maximum likelihood asymptotic approximations to the distribution can often be good when the sample size is small.

Original language	English
Pages (from-to)	835-842
Number of pages	8
Journal	Journal of Applied Statistics
Volume	28
Issue number	7
DOIs	https://doi.org/10.1080/02664760120074924
Publication status	Published - 2001
Externally published	Yes

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1080/02664760120074924

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abstract = "The size distortion problem is clearly indicative of the small-sample approximation in the Markov-switching regression model. This paper shows that the bootstrap procedure can relieve the effects that this problem has. Our Monte Carlo simulation results reveal that the bootstrap maximum likelihood asymptotic approximations to the distribution can often be good when the sample size is small.",

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AB - The size distortion problem is clearly indicative of the small-sample approximation in the Markov-switching regression model. This paper shows that the bootstrap procedure can relieve the effects that this problem has. Our Monte Carlo simulation results reveal that the bootstrap maximum likelihood asymptotic approximations to the distribution can often be good when the sample size is small.

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Finite-sample properties of the bootstrap estimator in a Markov-switching model

Abstract

ASJC Scopus subject areas

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