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Nonparametric identification of accelerated failure time competing risks models

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  • Sokbae Lee

    () (Institute for Fiscal Studies and Columbia University and IFS)

  • Arthur Lewbel

    () (Institute for Fiscal Studies and Boston College)

Abstract

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," CeMMAP working papers CWP14/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:14/10
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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. Effraimidis, Georgios, 2016. "Nonparametric Identification of a Time-Varying Frailty Model," COHERE Working Paper 2016:6, COHERE - Centre of Health Economics Research, University of Southern Denmark.
    3. 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.

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