IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2002.04470.html
   My bibliography  Save this paper

Generalized Poisson Difference Autoregressive Processes

Author

Listed:
  • Giulia Carallo
  • Roberto Casarin
  • Christian P. Robert

Abstract

This paper introduces a new stochastic process with values in the set Z of integers with sign. The increments of process are Poisson differences and the dynamics has an autoregressive structure. We study the properties of the process and exploit the thinning representation to derive stationarity conditions and the stationary distribution of the process. We provide a Bayesian inference method and an efficient posterior approximation procedure based on Monte Carlo. Numerical illustrations on both simulated and real data show the effectiveness of the proposed inference.

Suggested Citation

  • Giulia Carallo & Roberto Casarin & Christian P. Robert, 2020. "Generalized Poisson Difference Autoregressive Processes," Papers 2002.04470, arXiv.org.
  • Handle: RePEc:arx:papers:2002.04470
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2002.04470
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hossein Zamani & Pouya Faroughi & Noriszura Ismail, 2016. "Bivariate generalized Poisson regression model: applications on health care data," Empirical Economics, Springer, vol. 51(4), pages 1607-1621, December.
    2. Du Jin‐Guan & Li Yuan, 1991. "THE INTEGER‐VALUED AUTOREGRESSIVE (INAR(p)) MODEL," Journal of Time Series Analysis, Wiley Blackwell, vol. 12(2), pages 129-142, March.
    3. Hee-Young Kim & Yousung Park, 2008. "A non-stationary integer-valued autoregressive model," Statistical Papers, Springer, vol. 49(3), pages 485-502, July.
    4. Roman Liesenfeld & Ingmar Nolte & Winfried Pohlmeier, 2008. "Modelling financial transaction price movements: a dynamic integer count data model," Studies in Empirical Economics, in: Luc Bauwens & Winfried Pohlmeier & David Veredas (ed.), High Frequency Financial Econometrics, pages 167-197, Springer.
    5. René Ferland & Alain Latour & Driss Oraichi, 2006. "Integer‐Valued GARCH Process," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(6), pages 923-942, November.
    6. HEINEN, Andreas & RENGIFO, Erick, 2003. "Multivariate modelling of time series count data: an autoregressive conditional Poisson model," LIDAM Discussion Papers CORE 2003025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Alain Latour, 1998. "Existence and Stochastic Structure of a Non‐negative Integer‐valued Autoregressive Process," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(4), pages 439-455, July.
    8. Tina Hviid Rydberg & Neil Shephard, 2003. "Dynamics of Trade-by-Trade Price Movements: Decomposition and Models," Journal of Financial Econometrics, Oxford University Press, vol. 1(1), pages 2-25.
    9. R. K. Freeland & B. P. M. McCabe, 2004. "Analysis of low count time series data by poisson autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 701-722, September.
    10. Brendan P. M. McCabe & Gael M. Martin & David Harris, 2011. "Efficient probabilistic forecasts for counts," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(2), pages 253-272, March.
    11. Christian Weiß, 2009. "Modelling time series of counts with overdispersion," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(4), pages 507-519, November.
    12. McCabe, B.P.M. & Martin, G.M., 2005. "Bayesian predictions of low count time series," International Journal of Forecasting, Elsevier, vol. 21(2), pages 315-330.
    13. R. Freeland, 2010. "True integer value time series," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(3), pages 217-229, September.
    14. HEINEN, Andréas, 2003. "Modelling time series count data: an autoregressive conditional Poisson model," LIDAM Discussion Papers CORE 2003062, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    15. Felix Famoye & P. Consul, 1995. "Bivariate generalized Poisson distribution with some applications," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 42(1), pages 127-138, December.
    16. Felix Famoye, 2015. "A Multivariate Generalized Poisson Regression Model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(3), pages 497-511, February.
    17. Tina Hviid Rydberg & Neil Shephard, 2000. "BIN Models for Trade-by-Trade Data. Modelling the Number of Trades in a Fixed Interval of Time," Econometric Society World Congress 2000 Contributed Papers 0740, Econometric Society.
    18. Golnaz Shahtahmassebi & Rana Moyeed, 2016. "An application of the generalized Poisson difference distribution to the Bayesian modelling of football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(3), pages 260-273, August.
    19. Weiren Wang & Felix Famoye, 1997. "Modeling household fertility decisions with generalized Poisson regression," Journal of Population Economics, Springer;European Society for Population Economics, vol. 10(3), pages 273-283.
    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. Huaping Chen, 2023. "A New Soft-Clipping Discrete Beta GARCH Model and Its Application on Measles Infection," Stats, MDPI, vol. 6(1), pages 1-19, February.

    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. Federico Bassetti & Giulia Carallo & Roberto Casarin, 2022. "First-order integer-valued autoregressive processes with Generalized Katz innovations," Papers 2202.02029, arXiv.org.
    2. Aknouche, Abdelhakim & Dimitrakopoulos, Stefanos, 2020. "On an integer-valued stochastic intensity model for time series of counts," MPRA Paper 105406, University Library of Munich, Germany.
    3. Aknouche, Abdelhakim & Gouveia, Sonia & Scotto, Manuel, 2023. "Random multiplication versus random sum: auto-regressive-like models with integer-valued random inputs," MPRA Paper 119518, University Library of Munich, Germany, revised 18 Dec 2023.
    4. Mirko Armillotta & Paolo Gorgi, 2023. "Pseudo-variance quasi-maximum likelihood estimation of semi-parametric time series models," Tinbergen Institute Discussion Papers 23-054/III, Tinbergen Institute.
    5. Jung, Robert C. & Kukuk, Martin & Liesenfeld, Roman, 2006. "Time series of count data: modeling, estimation and diagnostics," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2350-2364, December.
    6. Christian H. Weiß & Esmeralda Gonçalves & Nazaré Mendes Lopes, 2017. "Testing the compounding structure of the CP-INARCH model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(5), pages 571-603, July.
    7. Christian H. Weiß, 2017. "On Eigenvalues of the Transition Matrix of Some Count-Data Markov Chains," Methodology and Computing in Applied Probability, Springer, vol. 19(3), pages 997-1007, September.
    8. Xu, Hai-Yan & Xie, Min & Goh, Thong Ngee & Fu, Xiuju, 2012. "A model for integer-valued time series with conditional overdispersion," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4229-4242.
    9. Christian H. Weiß & Sebastian Schweer, 2015. "Detecting overdispersion in INARCH(1) processes," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(3), pages 281-297, August.
    10. Axel Groß‐KlußMann & Nikolaus Hautsch, 2013. "Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(8), pages 724-742, December.
    11. Aknouche, Abdelhakim & Scotto, Manuel, 2022. "A multiplicative thinning-based integer-valued GARCH model," MPRA Paper 112475, University Library of Munich, Germany.
    12. Weiß, Christian H., 2010. "INARCH(1) processes: Higher-order moments and jumps," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1771-1780, December.
    13. Cattivelli, Luca & Pirino, Davide, 2019. "A SHARP model of bid–ask spread forecasts," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1211-1225.
    14. Jung, Robert & Kukuk, Martin & Liesenfeld, Roman, 2005. "Time Series of Count Data: Modelling and Estimation," Economics Working Papers 2005-08, Christian-Albrechts-University of Kiel, Department of Economics.
    15. Abdelhakim Aknouche & Christian Francq, 2022. "Stationarity and ergodicity of Markov switching positive conditional mean models," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(3), pages 436-459, May.
    16. Aknouche, Abdelhakim & Almohaimeed, Bader & Dimitrakopoulos, Stefanos, 2020. "Forecasting transaction counts with integer-valued GARCH models," MPRA Paper 101779, University Library of Munich, Germany, revised 11 Jul 2020.
    17. Daniel L. R. Orozco & Lucas O. F. Sales & Luz M. Z. Fernández & André L. S. Pinho, 2021. "A new mixed first-order integer-valued autoregressive process with Poisson innovations," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(4), pages 559-580, December.
    18. Feigin, Paul D. & Gould, Phillip & Martin, Gael M. & Snyder, Ralph D., 2008. "Feasible parameter regions for alternative discrete state space models," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2963-2970, December.
    19. Weiß, Christian H. & Schweer, Sebastian, 2016. "Bias corrections for moment estimators in Poisson INAR(1) and INARCH(1) processes," Statistics & Probability Letters, Elsevier, vol. 112(C), pages 124-130.
    20. Ralph D. Snyder & Gael M. Martin & Phillip Gould & Paul D. Feigin, 2007. "An Assessment of Alternative State Space Models for Count Time Series," Monash Econometrics and Business Statistics Working Papers 4/07, Monash University, Department of Econometrics and Business Statistics.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2002.04470. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.