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A Monte Carlo study on non-parametric estimation of duration models with unobserved heterogeneity

Author

Listed:
  • Zhang, Tao

    (The Ragnar Frisch Centre for Economic Research)

Abstract
We conduct extensive Monte Carlo experiments on non-parametric estimations of duration models with unknown duration dependence and unknown mixing distribution for unobserved heterogeneity. We propose a full non-parametric maximum likelihood approach, based on time-varying lagged explanatory covariates from observational data. By utilising this data-based identification source, we find that both duration dependence and unobserved heterogeneity can be reliably estimated. Our Monte Carlo evidences show that variation in time-varying lagged explanatory variables contributes to the identification of both duration dependence and unobserved heterogeneity, especially when sample sizes are limited. For limited sample sizes, maximum penalised likelihood with information criteria seems to produce more accurate estimators than pure maximum likelihood. Our approach can be easily extended to multivariate competing risks model with dependent unobserved heterogeneities.

Suggested Citation

  • Zhang, Tao, 2003. "A Monte Carlo study on non-parametric estimation of duration models with unobserved heterogeneity," Memorandum 25/2003, Oslo University, Department of Economics.
    • Handle: RePEc:hhs:osloec:2003_025
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    File URL: http://www.sv.uio.no/econ/english/research/unpublished-works/working-papers/pdf-files/2003/Memo-25-2003.pdf
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    2. Pål Børing, 2015. "The effects of firms’ R&D and innovation activities on their survival: a competing risks analysis," Empirical Economics, Springer, vol. 49(3), pages 1045-1069, November.
    3. Marco Grazzi & Chiara Piccardo & Cecilia Vergari, 2022. "Turmoil over the crisis: innovation capabilities and firm exit," Small Business Economics, Springer, vol. 59(2), pages 537-564, August.
    4. Gaure, Simen & Roed, Knut & Zhang, Tao, 2007. "Time and causality: A Monte Carlo assessment of the timing-of-events approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1159-1195, December.
    5. Pål Børing, 2010. "Gamma Unobserved Heterogeneity and Duration Bias," Econometric Reviews, Taylor & Francis Journals, vol. 29(1), pages 1-19.
    6. Nicoletti, Cheti & Rondinelli, Concetta, 2010. "The (mis)specification of discrete duration models with unobserved heterogeneity: A Monte Carlo study," Journal of Econometrics, Elsevier, vol. 159(1), pages 1-13, November.
    7. Nicoletti, Cheti & Rondinelli, Concetta, 2006. "The (mis)specification of discrete time duration models with unobserved heterogenity: a Monte Carlo study," ISER Working Paper Series 2006-53, Institute for Social and Economic Research.
    8. Ott-Siim Toomet, 2005. "Does an Increase in Unemployment Income Lead to Longer Unemployment Spells? Evidence Using Danish Unemployment Assistance Data," Bank of Estonia Working Papers 2005-09, Bank of Estonia, revised 10 Oct 2005.
    9. Ott Toomet, 2005. "Does an increase in unemployment income lead to longer unemployment spells? Evidence using Danish unemployment assistance data," Economics Working Papers 2005-07, Department of Economics and Business Economics, Aarhus University.

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    More about this item

    Keywords

    duration dependence; unobserved heterogeneity; non-parametric estimation; Monte Carlo study; time-varying covariates;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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