Nonparametric model validations for hidden Markov models with applications in financial econometrics

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous-time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.

Original languageEnglish (US)
Pages (from-to)225-239
Number of pages15
JournalJournal of Econometrics
Volume162
Issue number2
DOIs
StatePublished - Jun 1 2011

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Model validation
Nonparametric model
Hidden Markov model
Financial econometrics
Confidence
Continuous time
Kernel
Stochastic volatility model
Specification test
Nonlinear time series
Time series models
Market microstructure noise
Diffusion model
Density function

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

Cite this

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Nonparametric model validations for hidden Markov models with applications in financial econometrics. / Zhao, Zhibiao.

In: Journal of Econometrics, Vol. 162, No. 2, 01.06.2011, p. 225-239.

Research output: Contribution to journalArticle

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