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Nonparametric Identification of Accelerated Failure Time Competing Risks Models


  • Sokbae Lee

    (University College London)

  • Arthur Lewbel

    () (Boston College)


We provide new conditions for identification of accelerated failure time competing risks models. These include Roy models and some auction models. In our set up, unknown regression functions and the joint survivor function of latent disturbance terms are all nonparametric. We show that this model is identified given covariates that are independent of latent errors, provided that a certain rank condition is satisfied. We present a simple example in which our rank condition for identification is verified. Our identification strategy does not depend on identification at infinity or near zero, and it does not require exclusion assumptions. Given our identification, we show estimation can be accomplished using sieves.

Suggested Citation

  • Sokbae Lee & Arthur Lewbel, 2010. "Nonparametric Identification of Accelerated Failure Time Competing Risks Models," Boston College Working Papers in Economics 755, Boston College Department of Economics, revised 30 Jun 2011.
  • Handle: RePEc:boc:bocoec:755

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    Cited by:

    1. Ruixuan Liu, 2016. "A Competing Risks Model with Time-varying Heterogeneity and Simultaneous Failure," Emory Economics 1603, Department of Economics, Emory University (Atlanta).
    2. Áureo de Paula & Imran Rasul & Pedro Souza, 2018. "Recovering Social Networks from Panel Data: Identification, Simulations and an Application," Working Papers 2018-013, Human Capital and Economic Opportunity Working Group.
    3. Effraimidis, Georgios, 2016. "Nonparametric Identification of a Time-Varying Frailty Model," COHERE Working Paper 2016:6, University of Southern Denmark, COHERE - Centre of Health Economics Research.
    4. Komarova, Tatiana, 2017. "Extremum sieve estimation in k-out-of-n system," LSE Research Online Documents on Economics 79388, London School of Economics and Political Science, LSE Library.

    More about this item


    accelerated failure time models; competing risks; identifiability.;

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