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INARCH(1) processes: Higher-order moments and jumps

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  • Weiß, Christian H.
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    Abstract

    The INARCH(1) model is a simple but practically relevant, two-parameter model for processes of overdispersed counts with an autoregressive serial dependence structure. We derive closed-form expressions for the joint (central) moments and cumulants of the INARCH(1) model up to order 4. These expressions are applied to derive the moments of jumps in INARCH(1) processes. We illustrate this kind of application with a real-data example, and outline further potential applications.

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    Bibliographic Info

    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 80 (2010)
    Issue (Month): 23-24 (December)
    Pages: 1771-1780

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    Handle: RePEc:eee:stapro:v:80:y:2010:i:23-24:p:1771-1780

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    Related research

    Keywords: Count-data time series Cumulants INARCH(1) model Jumps moments Overdispersion;

    References

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    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    1. Zhu, Fukang & Wang, Dehui, 2010. "Diagnostic checking integer-valued ARCH(p) models using conditional residual autocorrelations," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 496-508, February.
    2. 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.
    3. Christian Weiß, 2009. "Modelling time series of counts with overdispersion," Statistical Methods and Applications, Springer, vol. 18(4), pages 507-519, November.
    4. Christian Weiß, 2008. "Thinning operations for modeling time series of counts—a survey," AStA Advances in Statistical Analysis, Springer, vol. 92(3), pages 319-341, August.
    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. HEINEN, Andreas & RENGIFO, Erick, 2003. "Multivariate modelling of time series count data: an autoregressive conditional Poisson model," CORE Discussion Papers 2003025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. HEINEN, Andréas, 2003. "Modelling time series count data: an autoregressive conditional Poisson model," CORE Discussion Papers 2003062, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Fokianos, Konstantinos & Rahbek, Anders & Tjøstheim, Dag, 2009. "Poisson Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1430-1439.
    9. Weiß, Christian H., 2009. "Jumps in binomial AR(1) processes," Statistics & Probability Letters, Elsevier, vol. 79(19), pages 2012-2019, October.
    10. Konstantinos Fokianos & Roland Fried, 2010. "Interventions in INGARCH processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(3), pages 210-225, 05.
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