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A sickle transition-rate model with starting threshold

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  • Francesco C. Billari

    (Max Planck Institute for Demographic Research)

Abstract

Billari (2001) introduced a new type of single-spell parametric transition-rate model: transition-rate models with a starting threshold. In such models, the transition-rate function is composed of two additive terms. The first term is a constant that holds for any given duration; the second is a ‘traditional’ transition-rate function with the threshold as its time origin, and it is added after a certain threshold point. The possibility of allowing for the presence of long-term survivors in the social process has not yet been dealt with, and it is of specific interest in several domains of application. In this paper, we develop the specific case of the sickle model. We discuss its features, its implementation as a starting threshold model, and the estimation of its parameters. The sickle model with starting threshold is then applied to the union formation of Italian men and women, using the Fertility and Family Survey data.

Suggested Citation

  • Francesco C. Billari, 2001. "A sickle transition-rate model with starting threshold," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 10(1), pages 139-155, January.
    • Handle: RePEc:spr:stmapp:v:10:y:2001:i:1:d:10.1007_bf02511645
    DOI: 10.1007/BF02511645
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    References listed on IDEAS

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    1. Ursula Henz & Johannes Huinink, 1999. "Problems concerning the parametric analysis of the age at first birth," Mathematical Population Studies, Taylor & Francis Journals, vol. 7(2), pages 131-145.
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    4. Josef Brãœederl & Andreas Diekmann, 1995. "The Log-Logistic Rate Model," Sociological Methods & Research, , vol. 24(2), pages 158-186, November.
    5. Francesco C. Billari & Hans-Peter Kohler, 2000. "The impact of union formation dynamics on first births in West Germany and Italy: are there signs of convergence?," MPIDR Working Papers WP-2000-008, Max Planck Institute for Demographic Research, Rostock, Germany.
    6. Chris T. Volinsky & Adrian E. Raftery, 2000. "Bayesian Information Criterion for Censored Survival Models," Biometrics, The International Biometric Society, vol. 56(1), pages 256-262, March.
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    Cited by:

    1. Nicoletta Balbo & Francesco C. Billari & Melinda Mills, 2013. "Fertility in Advanced Societies: A Review of Research," European Journal of Population, Springer;European Association for Population Studies, vol. 29(1), pages 1-38, February.

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