IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v26y1999i7p855-869.html
   My bibliography  Save this article

Markov Poisson regression models for discrete time series. Part 1: Methodology

Author

Listed:
  • Peiming Wang
  • Martin Puterman

Abstract

This paper proposes and investigates a class of Markov Poisson regression models in which Poisson rate functions of covariates are conditional on unobserved states which follow a finite-state Markov chain. Features of the proposed model, estimation, inference, bootstrap confidence intervals, model selection and other implementation issues are discussed. Monte Carlo studies suggest that the proposed estimation method is accurate and reliable for single- and multiple-subject time series data; the choice of starting probabilities for the Markov process has little eff ect on the parameter estimates; and penalized likelihood criteria are reliable for determining the number of states. Part 2 provides applications of the proposed model.

Suggested Citation

  • Peiming Wang & Martin Puterman, 1999. "Markov Poisson regression models for discrete time series. Part 1: Methodology," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(7), pages 855-869.
    • Handle: RePEc:taf:japsta:v:26:y:1999:i:7:p:855-869
    DOI: 10.1080/02664769922098
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922098
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664769922098?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Leroux, Brian G., 1992. "Maximum-likelihood estimation for hidden Markov models," Stochastic Processes and their Applications, Elsevier, vol. 40(1), pages 127-143, February.
    2. Wang, Peiming & Cockburn, Iain M & Puterman, Martin L, 1998. "Analysis of Patent Data--A Mixed-Poisson-Regression-Model Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 27-41, January.
    3. Peiming Wang & Martin Puterman, 1999. "Markov Poisson regression models for discrete time series. Part 2: Applications," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(7), pages 871-882.
    4. Peiming Wang & Iain Cockburn & Martin L. Puterman, "undated". "A Mixed Poisson Regression Model for Analysis of Patent Data," Computing in Economics and Finance 1996 _049, Society for Computational Economics.
    5. Ludwig Fahrmeir & Heinz Kaufmann, 1987. "Regression Models For Non‐Stationary Categorical Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 8(2), pages 147-160, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pami Dua & Divya Tuteja, 2021. "Regime Shifts in the Behaviour of International Currency and Equity Markets: A Markov-Switching Analysis," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 309-336, December.
    2. Peiming Wang & Martin Puterman, 1999. "Markov Poisson regression models for discrete time series. Part 2: Applications," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(7), pages 871-882.
    3. Park, Jeong-gun & Basawa, I. V., 2002. "Estimation for mixtures of Markov processes," Statistics & Probability Letters, Elsevier, vol. 59(3), pages 235-244, October.
    4. Dannemann, Jörn & Holzmann, Hajo, 2010. "Testing for two components in a switching regression model," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1592-1604, June.

    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. Greene, William, 2007. "Functional Form and Heterogeneity in Models for Count Data," Foundations and Trends(R) in Econometrics, now publishers, vol. 1(2), pages 113-218, August.
    2. Deb, Partha & Trivedi, Pravin K., 2002. "The structure of demand for health care: latent class versus two-part models," Journal of Health Economics, Elsevier, vol. 21(4), pages 601-625, July.
    3. Payandeh Najafabadi Amir T. & MohammadPour Saeed, 2018. "A k-Inflated Negative Binomial Mixture Regression Model: Application to Rate–Making Systems," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 12(2), pages 1-31, July.
    4. Padmaja Ayyagari & Partha Deb & Jason Fletcher & William Gallo & Jody L. Sindelar, 2013. "Understanding Heterogeneity In Price Elasticities In The Demand For Alcohol For Older Individuals," Health Economics, John Wiley & Sons, Ltd., vol. 22(1), pages 89-105, January.
    5. Chadha, Alka, 2009. "TRIPs and patenting activity: Evidence from the Indian pharmaceutical industry," Economic Modelling, Elsevier, vol. 26(2), pages 499-505, March.
    6. Partha Deb & William T. Gallo & Padmaja Ayyagari & Jason M. Fletcher & Jody L. Sindelar, 2009. "Job Loss: Eat, drink and try to be merry?," NBER Working Papers 15122, National Bureau of Economic Research, Inc.
    7. William Greene, 2007. "Correlation in Bivariate Poisson Regression Model," Working Papers 07-14, New York University, Leonard N. Stern School of Business, Department of Economics.
    8. Herriges, Joseph A. & Phaneuf, Daniel J. & Tobias, Justin L., 2008. "Estimating demand systems when outcomes are correlated counts," Journal of Econometrics, Elsevier, vol. 147(2), pages 282-298, December.
    9. Stephen Hynes & William Greene, 2016. "Preference Heterogeneity in Contingent Behaviour Travel Cost Models with On-site Samples: A Random Parameter vs. a Latent Class Approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 67(2), pages 348-367, June.
    10. Shiferaw Gurmu & Fidel Pérez-Sebastián, 2008. "Patents, R&D and lag effects: evidence from flexible methods for count panel data on manufacturing firms," Empirical Economics, Springer, vol. 35(3), pages 507-526, November.
    11. Padmaja Ayyagari & Partha Deb & Jason Fletcher & William T. Gallo & Jody L. Sindelar, 2009. "Sin Taxes: Do Heterogeneous Responses Undercut Their Value?," NBER Working Papers 15124, National Bureau of Economic Research, Inc.
    12. Bermúdez, Lluís & Karlis, Dimitris, 2012. "A finite mixture of bivariate Poisson regression models with an application to insurance ratemaking," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 3988-3999.
    13. Eduardo Fé & Richard Hofler, 2013. "Count data stochastic frontier models, with an application to the patents–R&D relationship," Journal of Productivity Analysis, Springer, vol. 39(3), pages 271-284, June.
    14. Dimitris Karlis & Purushottam Papatla & Sudipt Roy, 2016. "Finite mixtures of censored Poisson regression models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(2), pages 100-122, May.
    15. Vincenzo Atella & Francesco Brindisi & Partha Deb & Furio C. Rosati, 2004. "Determinants of access to physician services in Italy: a latent class seemingly unrelated probit approach," Health Economics, John Wiley & Sons, Ltd., vol. 13(7), pages 657-668, July.
    16. Conway, Karen Smith & Deb, Partha, 2005. "Is prenatal care really ineffective? Or, is the 'devil' in the distribution?," Journal of Health Economics, Elsevier, vol. 24(3), pages 489-513, May.
    17. Lim, Hwa Kyung & Li, Wai Keung & Yu, Philip L.H., 2014. "Zero-inflated Poisson regression mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 151-158.
    18. Drivas, Kyriakos & Economidou, Claire & Tsionas, Efthymios G., 2014. "A Poisson Stochastic Frontier Model with Finite Mixture Structure," MPRA Paper 57485, University Library of Munich, Germany.
    19. Greene, William, 2008. "Functional forms for the negative binomial model for count data," Economics Letters, Elsevier, vol. 99(3), pages 585-590, June.
    20. Dalrymple, M. L. & Hudson, I. L. & Ford, R. P. K., 2003. "Finite Mixture, Zero-inflated Poisson and Hurdle models with application to SIDS," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 491-504, January.

    More about this item

    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:taf:japsta:v:26:y:1999:i:7:p:855-869. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.