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Statistical analysis of heaped duration data

Author

Listed:
  • Petoussis, Kos
  • Gill, Richard
  • Zeelenberg, Kees

Abstract

This paper shows how heaping of duration data, e.g. caused by rounding due to memory effects, can be analyzed. If the data are heaped Cox's partial likelihood approach, which is often used in survival analysis, is no longer appropriate. We show how this problem can be overcome by considering the problem as a missing data problem. A variant of Cox's Proportional Hazard Model is constructed that takes heaping into account, and is estimated by maximum likelihood using the EM algorithm, with many nuisance parameters, simultaneously for all parameters. Ingredients of our method are application of the EM algorithm, Cox regression and nonparametric maximum likelihood calculation with `predicted' data in each M step. An example from practice, where jackknife is used to estimate the variances, illustrates the power of the new methodology.

Suggested Citation

  • Petoussis, Kos & Gill, Richard & Zeelenberg, Kees, 1997. "Statistical analysis of heaped duration data," MPRA Paper 89263, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:89263
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    File URL: https://mpra.ub.uni-muenchen.de/89263/1/MPRA_paper_89263.pdf
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    References listed on IDEAS

    as
    1. Torelli, Nicola & Trivellato, Ugo, 1993. "Modelling inaccuracies in job-search duration data," Journal of Econometrics, Elsevier, vol. 59(1-2), pages 187-211, September.
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    Cited by:

    1. Arulampalam, Wiji & Corradi, Valentina & Gutknecht, Daniel, 2017. "Modeling heaped duration data: An application to neonatal mortality," Journal of Econometrics, Elsevier, vol. 200(2), pages 363-377.
    2. Byung-hill Jun & Hosin Song, 2019. "Tests for Detecting Probability Mass Points," Korean Economic Review, Korean Economic Association, vol. 35, pages 205-248.

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    More about this item

    Keywords

    heaping; duration data; survival analysis; Proportional Hazard Model; profile likelihood; EM algorithm;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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