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Sequential testing of duration data: the case of the Pennsylvania 'reemployment bonus' experiment

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  • Yannis Bilias

    (Department of Economics, University of Cyprus, PO Box 20537, 1678 Nicosia, Cyprus)

Abstract

Cost considerations and the need to report the results promptly make it desirable to examine data as it accumulates and to terminate an experimental study as soon as definite statistical conclusions can be drawn. These ideas are illustrated in a retrospective analysis of the Pennsylvania 'Reemployment Bonus' experiment. Recently developed large-sample theory for a variety of sequentially computed score processes of time-to-event (duration) data with staggered entry enables one to construct stopping boundaries of a prescribed level of significance. The gains from the use of sequential methodology appear to be worth while. Copyright © 2000 John Wiley & Sons, Ltd.

Suggested Citation

  • Yannis Bilias, 2000. "Sequential testing of duration data: the case of the Pennsylvania 'reemployment bonus' experiment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 575-594.
    • Handle: RePEc:jae:japmet:v:15:y:2000:i:6:p:575-594
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    References listed on IDEAS

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    Cited by:

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    2. van der Klaauw, B. & van Ours, J.C., 2010. "Carrot and Stick : How Reemployment Bonuses and Benefit Sanctions Affect Job Finding Rates," Discussion Paper 2010-66, Tilburg University, Center for Economic Research.
    3. Anil K. Bera & Yannis Bilias, 2024. "Three Scores and 15 Years (1948-2023) of Rao's Score Test: A Brief History," Papers 2406.19956, arXiv.org, revised Oct 2024.
    4. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    5. Anders Bredahl Kock & David Preinerstorfer, 2024. "Regularizing Fairness in Optimal Policy Learning with Distributional Targets," Papers 2401.17909, arXiv.org, revised May 2025.
    6. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    7. Maria Nareklishvili & Nicholas Polson & Vadim Sokolov, 2022. "Feature Selection for Personalized Policy Analysis," Papers 2301.00251, arXiv.org, revised Jul 2023.

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