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A new class of integer-valued GARCH models for time series of bounded counts with extra-binomial variation

Author

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  • Huaping Chen

    (Henan University)

  • Qi Li

    (Changchun Normal University)

  • Fukang Zhu

    (Jilin University)

Abstract

This article considers a modeling problem of integer-valued time series of bounded counts in which the binomial index of dispersion of the observations is greater than one, i.e., the observations inhere the characteristic of extra-binomial variation. Most methods analyzing such characteristic are based on the conditional mean process instead of the observed process itself. To fill this gap, we introduce a new class of beta-binomial integer-valued GARCH models, establish the geometric moment contracting property of its conditional mean process, discuss the stationarity and ergodicity of the observed process and its conditional mean process, and give some stochastic properties of them. We consider the conditional maximum likelihood estimates and establish the asymptotic properties of the estimators. The performances of these estimators are compared via simulation studies. Finally, we apply the proposed models to two real data sets.

Suggested Citation

  • Huaping Chen & Qi Li & Fukang Zhu, 2022. "A new class of integer-valued GARCH models for time series of bounded counts with extra-binomial variation," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(2), pages 243-270, June.
  • Handle: RePEc:spr:alstar:v:106:y:2022:i:2:d:10.1007_s10182-021-00414-8
    DOI: 10.1007/s10182-021-00414-8
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    References listed on IDEAS

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    1. Fokianos, Konstantinos & Rahbek, Anders & Tjøstheim, Dag, 2009. "Poisson Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1430-1439.
    2. F. Blasques & S. J. Koopman & A. Lucas, 2015. "Information-theoretic optimality of observation-driven time series models for continuous responses," Biometrika, Biometrika Trust, vol. 102(2), pages 325-343.
    3. Christian Weiß, 2008. "Thinning operations for modeling time series of counts—a survey," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(3), pages 319-341, August.
    4. Christian H. Weiß & Philip K. Pollett, 2014. "Binomial Autoregressive Processes With Density-Dependent Thinning," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(2), pages 115-132, March.
    5. Agosto, Arianna & Cavaliere, Giuseppe & Kristensen, Dennis & Rahbek, Anders, 2016. "Modeling corporate defaults: Poisson autoregressions with exogenous covariates (PARX)," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 640-663.
    6. Christian H. Weiß & Murat Caner Testik, 2015. "On the Phase I analysis for monitoring time-dependent count processes," IISE Transactions, Taylor & Francis Journals, vol. 47(3), pages 294-306, March.
    7. Fukang Zhu & Shuangzhe Liu & Lei Shi, 2016. "Local influence analysis for Poisson autoregression with an application to stock transaction data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(1), pages 4-25, February.
    8. Youngmi Lee & Sangyeol Lee, 2019. "CUSUM test for general nonlinear integer-valued GARCH models: comparison study," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1033-1057, October.
    9. Fukang Zhu & Lei Shi & Shuangzhe Liu, 2015. "Influence diagnostics in log-linear integer-valued GARCH models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(3), pages 311-335, July.
    10. Huaping Chen & Qi Li & Fukang Zhu, 2021. "Binomial AR(1) processes with innovational outliers," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(2), pages 446-472, January.
    11. Heejoon Han & Dennis Kristensen, 2014. "Asymptotic Theory for the QMLE in GARCH-X Models With Stationary and Nonstationary Covariates," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 416-429, July.
    12. Jensen, Søren Tolver & Rahbek, Anders, 2004. "Asymptotic Inference For Nonstationary Garch," Econometric Theory, Cambridge University Press, vol. 20(6), pages 1203-1226, December.
    13. Francisco Blasques & André Lucas & Erkki Silde, 2018. "A stochastic recurrence equations approach for score driven correlation models," Econometric Reviews, Taylor & Francis Journals, vol. 37(2), pages 166-181, February.
    14. Marco Bazzi & Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2017. "Time-Varying Transition Probabilities for Markov Regime Switching Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(3), pages 458-478, May.
    15. Olivier Wintenberger, 2013. "Continuous Invertibility and Stable QML Estimation of the EGARCH(1,1) Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 846-867, December.
    16. Paolo Gorgi, 2018. "Integer†Valued Autoregressive Models With Survival Probability Driven By A Stochastic Recurrence Equation," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(2), pages 150-171, March.
    17. Christian H. Weiß & Hee‐Young Kim, 2014. "Diagnosing and modeling extra‐binomial variation for time‐dependent counts," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 30(5), pages 588-608, September.
    18. Tobias A. Möller & Christian H. Weiß & Hee-Young Kim & Andrei Sirchenko, 2018. "Modeling Zero Inflation in Count Data Time Series with Bounded Support," Methodology and Computing in Applied Probability, Springer, vol. 20(2), pages 589-609, June.
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