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Detecting Structural Breaks using Hidden Markov Models


  • Christos Ntantamis

    (School of Economics and Management, University of Aarhus and CREATES)


Testing for structural breaks and identifying their location is essential for econometric modeling. In this paper, a Hidden Markov Model (HMM) approach is used in order to perform these tasks. Breaks are defined as the data points where the underlying Markov Chain switches from one state to another. The estimation of the HMM is conducted using a variant of the Iterative Conditional Expectation-Generalized Mixture (ICE-GEMI) algorithm proposed by Delignon et al. (1997), that permits analysis of the conditional distributions of economic data and allows for different functional forms across regimes. The locations of the breaks are subsequently obtained by assigning states to data points according to the Maximum Posterior Mode (MPM) algorithm. The Integrated Classification Likelihood-Bayesian Information Criterion (ICL-BIC) allows for the determination of the number of regimes by taking into account the classification of the data points to their corresponding regimes. The performance of the overall procedure, denoted IMI by the initials of the component algorithms, is validated by two sets of simulations; one in which only the parameters are permitted to differ across regimes, and one that also permits differences in the functional forms. The IMI method performs well in both sets. Moreover, when it is compared to the Bai and Perron (1998) method its performance is superior in the assessing the number of breaks and their respective locations. Finally, the methodology is applied for the detection of breaks in the monetary policy of United States, the different functional form being variants of the Taylor (1993) rule.

Suggested Citation

  • Christos Ntantamis, 2010. "Detecting Structural Breaks using Hidden Markov Models," CREATES Research Papers 2010-52, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2010-52

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    References listed on IDEAS

    1. Dueker, Michael J, 1997. "Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 26-34, January.
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    Cited by:

    1. Derrode, St├ęphane & Pieczynski, Wojciech, 2013. "Unsupervised data classification using pairwise Markov chains with automatic copulas selection," Computational Statistics & Data Analysis, Elsevier, vol. 63(C), pages 81-98.

    More about this item


    Structural change; Hidden Markov Model; Regime Switching; Bayesian Segmentation; Monetary Policy;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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