Estimation of state-space models with endogenous Markov regime-switching parameters

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This study proposes and estimates state-space models with endogenous Markov regime-switching parameters. It complements regime-switching dynamic linear models by allowing the discrete regime to be jointly determined with observed or unobserved continuous state variables. The estimation framework involves a Bayesian Markov chain Monte Carlo scheme to simulate the latent state variable that controls the regime shifts. A simulation exercise shows that neglecting endogeneity leads to biased inference. This method is then applied to the dynamic Nelson-Siegel yield curve model where the unobserved time-varying level, slope and curvature factors are contemporaneously correlated with the Markov-switching volatility regimes. The estimation results indicate that the high volatility tends to be associated with positive innovations in the level and slope factors. More importantly, we find that the endogenous regime-switching dynamic Nelson-Siegel model outperforms the model with and without exogenous regime-switching in terms of out-of-sample prediction accuracy.

Original languageEnglish
Pages (from-to)56-82
Number of pages27
JournalEconometrics Journal
Volume17
Issue number1
DOIs
Publication statusPublished - 2014 Feb 1

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Regime switching
Markov regime-switching
State-space model
Factors
State variable
Curvature
Markov switching
Dynamic linear models
Regime shift
Endogeneity
Yield curve
Exercise
Time-varying
Out-of-sample forecasting
Simulation
Nelson-Siegel model
Inference
Markov chain Monte Carlo
Innovation
Prediction accuracy

Keywords

  • Bayesian Markov chain Monte Carlo estimation
  • Dynamic Nelson-Siegel model
  • Marginal likelihood
  • Particle filter
  • Predictive accuracy

ASJC Scopus subject areas

  • Economics and Econometrics

Cite this

Estimation of state-space models with endogenous Markov regime-switching parameters. / Kang, Kyu Ho.

In: Econometrics Journal, Vol. 17, No. 1, 01.02.2014, p. 56-82.

Research output: Contribution to journalArticle

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