IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v209y2025ics0167947325000477.html
   My bibliography  Save this article

Hidden semi-Markov models with inhomogeneous state dwell-time distributions

Author

Listed:
  • Koslik, Jan-Ole

Abstract

The well-established methodology for the estimation of hidden semi-Markov models (HSMMs) as hidden Markov models (HMMs) with extended state spaces is further developed. Covariate influences are incorporated across all aspects of the state process model, in particular regarding the distributions governing the state dwell time. The special case of periodically varying covariate effects on the state dwell-time distributions — and possibly the conditional transition probabilities — is examined in detail. Important properties of these models are derived, including the periodically varying unconditional state distribution as well as the overall state dwell-time distribution. Simulation studies are conducted to assess key properties of these models and provide recommendations for hyperparameter settings. A case study involving an HSMM with periodically varying dwell-time distributions is presented to analyse the movement trajectory of an Arctic muskox, demonstrating the practical relevance of the developed methodology.

Suggested Citation

  • Koslik, Jan-Ole, 2025. "Hidden semi-Markov models with inhomogeneous state dwell-time distributions," Computational Statistics & Data Analysis, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:csdana:v:209:y:2025:i:c:s0167947325000477
    DOI: 10.1016/j.csda.2025.108171
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947325000477
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2025.108171?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Pohle, Jennifer & Adam, Timo & Beumer, Larissa T., 2022. "Flexible estimation of the state dwell-time distribution in hidden semi-Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    2. Turner, Christopher M. & Startz, Richard & Nelson, Charles R., 1989. "A Markov model of heteroskedasticity, risk, and learning in the stock market," Journal of Financial Economics, Elsevier, vol. 25(1), pages 3-22, November.
    3. Chen, Yiyang & Mamon, Rogemar & Spagnolo, Fabio & Spagnolo, Nicola, 2022. "Renewable energy and economic growth: A Markov-switching approach," Energy, Elsevier, vol. 244(PB).
    4. Bulla, Jan & Bulla, Ingo, 2006. "Stylized facts of financial time series and hidden semi-Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2192-2209, December.
    5. Maheu, John M & McCurdy, Thomas H, 2000. "Identifying Bull and Bear Markets in Stock Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 100-112, January.
    6. Benny Ren & Ian Barnett, 2023. "Combining mixed effects hidden Markov models with latent alternating recurrent event processes to model diurnal active–rest cycles," Biometrics, The International Biometric Society, vol. 79(4), pages 3402-3417, December.
    7. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Advances in Econometrics, in: Missing Data Methods: Time-Series Methods and Applications, pages 1-86, Emerald Group Publishing Limited.
    8. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    9. Abdul Rahman & Muhammad Arshad Khan & Lanouar Charfeddine & David McMillan, 2020. "Financial development–economic growth nexus in Pakistan: new evidence from the Markov switching model," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1716446-171, January.
    10. Stoner, Oliver & Economou, Theo, 2020. "An advanced hidden Markov model for hourly rainfall time series," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    11. John M. Maheu & Thomas H. McCurdy, 2002. "Nonlinear Features of Realized FX Volatility," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 668-681, November.
    12. Jennifer Pohle & Roland Langrock & Floris M. Beest & Niels Martin Schmidt, 2017. "Selecting the Number of States in Hidden Markov Models: Pragmatic Solutions Illustrated Using Animal Movement," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 270-293, September.
    13. Tobias Rydén & Timo Teräsvirta & Stefan Åsbrink, 1998. "Stylized facts of daily return series and the hidden Markov model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(3), pages 217-244.
    14. Langrock, R. & Zucchini, W., 2011. "Hidden Markov models with arbitrary state dwell-time distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 715-724, January.
    15. Iain L. MacDonald, 2014. "Numerical Maximisation of Likelihood: A Neglected Alternative to EM?," International Statistical Review, International Statistical Institute, vol. 82(2), pages 296-308, August.
    16. Liu, Xinyi & Margaritis, Dimitris & Wang, Peiming, 2012. "Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 483-496.
    17. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maruotti, Antonello & Petrella, Lea & Sposito, Luca, 2021. "Hidden semi-Markov-switching quantile regression for time series," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    2. Jan Bulla, 2010. "Hidden Markov models with t components. Increased persistence and other aspects," Quantitative Finance, Taylor & Francis Journals, vol. 11(3), pages 459-475.
    3. Liu, Xinyi & Margaritis, Dimitris & Wang, Peiming, 2012. "Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 483-496.
    4. Antonello Maruotti & Antonio Punzo, 2021. "Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies," International Statistical Review, International Statistical Institute, vol. 89(3), pages 447-480, December.
    5. Wasim Ahmad & N. Bhanumurthy & Sanjay Sehgal, 2015. "Regime dependent dynamics and European stock markets: Is asset allocation really possible?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 42(1), pages 77-107, February.
    6. Roland Langrock & Timo Adam & Vianey Leos‐Barajas & Sina Mews & David L. Miller & Yannis P. Papastamatiou, 2018. "Spline‐based nonparametric inference in general state‐switching models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(3), pages 179-200, August.
    7. Yizhan Shu & Chenyu Yu & John M. Mulvey, 2024. "Downside risk reduction using regime-switching signals: a statistical jump model approach," Journal of Asset Management, Palgrave Macmillan, vol. 25(5), pages 493-507, September.
    8. Yizhan Shu & Chenyu Yu & John M. Mulvey, 2024. "Downside Risk Reduction Using Regime-Switching Signals: A Statistical Jump Model Approach," Papers 2402.05272, arXiv.org, revised Sep 2024.
    9. Luca De Angelis & Leonard J. Paas, 2013. "A dynamic analysis of stock markets using a hidden Markov model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1682-1700, August.
    10. Rania Jammazi & Duc Khuong Nguyen, 2015. "Responses of international stock markets to oil price surges: a regime-switching perspective," Applied Economics, Taylor & Francis Journals, vol. 47(41), pages 4408-4422, September.
    11. Jan Bulla & Sascha Mergner & Ingo Bulla & André Sesboüé & Christophe Chesneau, 2011. "Markov-switching asset allocation: Do profitable strategies exist?," Journal of Asset Management, Palgrave Macmillan, vol. 12(5), pages 310-321, November.
    12. Adam, Timo & Mayr, Andreas & Kneib, Thomas, 2022. "Gradient boosting in Markov-switching generalized additive models for location, scale, and shape," Econometrics and Statistics, Elsevier, vol. 22(C), pages 3-16.
    13. Hanna, Alan J., 2018. "A top-down approach to identifying bull and bear market states," International Review of Financial Analysis, Elsevier, vol. 55(C), pages 93-110.
    14. Richard D. F. Harris & Murat Mazibas, 2022. "A component Markov regime‐switching autoregressive conditional range model," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 650-683, April.
    15. Milan Kumar Das & Anindya Goswami, 2019. "Testing of binary regime switching models using squeeze duration analysis," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 1-20, March.
    16. John M. Maheu & Thomas H. McCurdy, 2002. "Nonlinear Features of Realized FX Volatility," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 668-681, November.
    17. Bulla, Jan & Mergner, Sascha & Bulla, Ingo & Sesboüé, André & Chesneau, Christophe, 2010. "Markov-switching Asset Allocation: Do Profitable Strategies Exist?," MPRA Paper 21154, University Library of Munich, Germany.
    18. Shin-Yun Wang & Ming-Che Chuang & Shih-Kuei Lin & So-De Shyu, 2021. "Option pricing under stock market cycles with jump risks: evidence from the S&P 500 index," Review of Quantitative Finance and Accounting, Springer, vol. 56(1), pages 25-51, January.
    19. Erik Kole & Dick Dijk, 2017. "How to Identify and Forecast Bull and Bear Markets?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 120-139, January.
    20. Jeff Fleming & Chris Kirby, 2013. "Component-Driven Regime-Switching Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 11(2), pages 263-301, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:209:y:2025:i:c:s0167947325000477. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.