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A note on the Weibull distribution and time aggregation bias

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  • Knut Røed
  • Tao Zhang

Abstract

The application of continuous time Weibull models on discrete unemployment duration data may result in severely biased baseline estimates. The bias can be substantial even for weekly duration data, and it is seriously aggravated if the Weibull model is erroneously mixed with a Gamma distribution for unobserved heterogeneity.

Suggested Citation

  • Knut Røed & Tao Zhang, 2002. "A note on the Weibull distribution and time aggregation bias," Applied Economics Letters, Taylor & Francis Journals, vol. 9(7), pages 469-472.
    • Handle: RePEc:taf:apeclt:v:9:y:2002:i:7:p:469-472
    DOI: 10.1080/13504850110096340
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    References listed on IDEAS

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    1. Wiji Narendranathan & Mark B. Stewart, 1993. "Modelling the Probability of Leaving Unemployment: Competing Risks Models with Flexible Base‐Line Hazards," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 42(1), pages 63-83, March.
    2. Bergstrom, R & Edin, P-A, 1992. "Time Aggregation and the Distributional Shape of Unemployment Duration," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(1), pages 5-30, Jan.-Marc.
    3. Meyer, Bruce D, 1990. "Unemployment Insurance and Unemployment Spells," Econometrica, Econometric Society, vol. 58(4), pages 757-782, July.
    4. repec:bla:econom:v:62:y:1995:i:247:p:353-71 is not listed on IDEAS
    5. Aaberge, Rolf, 1996. "Unemployment duration models with non-stationary inflow and unobserved heterogeneity," Ricerche Economiche, Elsevier, vol. 50(2), pages 163-172, June.
    6. Knut Roed & Oddbjorn Raaum & Harald Goldstein, 1999. "Does unemployment cause unemployment? Micro evidence from Norway," Applied Economics, Taylor & Francis Journals, vol. 31(10), pages 1207-1218.
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    Cited by:

    1. Manudeep Bhuller & Christian N. Brinch & Sebastian Königs, 2017. "Time Aggregation and State Dependence in Welfare Receipt," Economic Journal, Royal Economic Society, vol. 127(604), pages 1833-1873, September.
    2. Yolanda M. Gómez & Diego I. Gallardo & Carolina Marchant & Luis Sánchez & Marcelo Bourguignon, 2023. "An In-Depth Review of the Weibull Model with a Focus on Various Parameterizations," Mathematics, MDPI, vol. 12(1), pages 1-19, December.
    3. Christian N. Brinch, 2011. "Non‐parametric identification of the mixed proportional hazards model with interval‐censored durations," Econometrics Journal, Royal Economic Society, vol. 14(2), pages 343-350, July.
    4. Wulfrano Gómez & Leovardo Mata & Montserrat Reyna, 2013. "Hodrick-Prescott Filter: An Extreme-Sport Testing," Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), Tecnológico de Monterrey, Campus Ciudad de México, vol. 7(1), pages 1-13.
    5. Christian N. Brinch, 2008. "Non-parametric Identification of the Mixed Hazards Model with Interval-Censored Durations," Discussion Papers 539, Statistics Norway, Research Department.

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    JEL classification:

    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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