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Regression Models for Binary Time Series

In: Modeling Uncertainty

Author

Listed:
  • Benjamin Kedem

    (University of Maryland)

  • Konstantinos Fokianos

    (University of Cyprus)

Abstract

We consider the general regression problem for binary time series where the covariates are stochastic and time dependent and the inverse link is any differentiable cumulative distribution function. This means that the popular logistic and probit regression models are special cases. The statistical analysis is carried out via partial likelihood estimation. Under a certain large sample assumption on the covariates, and owing to the fact that the score process is a martingale, the maximum partial likelihood estimator is consistent and asymptotically normal. From this we obtain the asymptotic distribution of a certain useful goodness of fit statistic.

Suggested Citation

  • Benjamin Kedem & Konstantinos Fokianos, 2002. "Regression Models for Binary Time Series," International Series in Operations Research & Management Science, in: Moshe Dror & Pierre L’Ecuyer & Ferenc Szidarovszky (ed.), Modeling Uncertainty, chapter 0, pages 185-199, Springer.
    • Handle: RePEc:spr:isochp:978-0-306-48102-4_9
    DOI: 10.1007/0-306-48102-2_9
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    Citations

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    Cited by:

    1. Fokianos, Konstantinos, 2024. "Multivariate Count Time Series Modelling," Econometrics and Statistics, Elsevier, vol. 31(C), pages 100-116.
    2. Paul Doukhan & Konstantinos Fokianos & Joseph Rynkiewicz, 2021. "Mixtures of Nonlinear Poisson Autoregressions," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(1), pages 107-135, January.
    3. Wilson Kalisa & Tertsea Igbawua & Fanan Ujoh & Igbalumun S. Aondoakaa & Jean Nepomuscene Namugize & Jiahua Zhang, 2021. "Spatio-temporal variability of dry and wet conditions over East Africa from 1982 to 2015 using quantile regression model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(3), pages 2047-2076, April.
    4. Xuze Zhang & Benjamin Kedem, 2021. "Extended residual coherence with a financial application," Statistics in Transition New Series, Polish Statistical Association, vol. 22(2), pages 1-14, June.
    5. Zhang Xuze & Kedem Benjamin, 2021. "Extended residual coherence with a financial application," Statistics in Transition New Series, Statistics Poland, vol. 22(2), pages 1-14, June.
    6. Luiza S. C. Piancastelli & Wagner Barreto‐Souza & Hernando Ombao, 2023. "Flexible bivariate INGARCH process with a broad range of contemporaneous correlation," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 206-222, March.
    7. Kharin, Yuriy & Voloshko, Valeriy, 2021. "Robust estimation for Binomial conditionally nonlinear autoregressive time series based on multivariate conditional frequencies," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    8. Yuhyeong Jang & Raanju R. Sundararajan & Wagner Barreto-Souza & Elizabeth Wheaton-Paramo, 2024. "Determining economic factors for sex trafficking in the United States using count time series regression," Empirical Economics, Springer, vol. 67(1), pages 337-354, July.
    9. Kirchner, Matthias & Torrisi, Giovanni Luca, 2023. "Fluctuations and precise deviations of cumulative INAR time series," Stochastic Processes and their Applications, Elsevier, vol. 164(C), pages 1-32.
    10. Fokianos, Konstantinos & Fried, Roland & Kharin, Yuriy & Voloshko, Valeriy, 2022. "Statistical analysis of multivariate discrete-valued time series," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    11. Vurukonda Sathish & Siuli Mukhopadhyay & Rashmi Tiwari, 2022. "Autoregressive and moving average models for zero‐inflated count time series," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(2), pages 190-218, May.
    12. Moritz Berger & Gerhard Tutz, 2021. "Transition models for count data: a flexible alternative to fixed distribution models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1259-1283, October.
    13. Wagner Barreto‐Souza & Hernando Ombao, 2022. "The negative binomial process: A tractable model with composite likelihood‐based inference," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 568-592, June.
    14. Mirko Armillotta & Konstantinos Fokianos, 2024. "Count network autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(4), pages 584-612, July.

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