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Nonparametric identification of accelerated failure time competing risks models

Author

Listed:
  • Sokbae (Simon) Lee

    (Institute for Fiscal Studies and Columbia University)

  • 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 (Simon) 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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    File URL: http://cemmap.ifs.org.uk/wps/cwp1410.pdf
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    Cited by:

    1. Ismaël Mourifié & Marc Henry & Romuald Méango, 2020. "Sharp Bounds and Testability of a Roy Model of STEM Major Choices," Journal of Political Economy, University of Chicago Press, vol. 128(8), pages 3220-3283.
    2. de Paula, Aureo & Rasul, Imran & Souza, Pedro, 2018. "Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition," CEPR Discussion Papers 12792, C.E.P.R. Discussion Papers.
    3. Kim, Dongwoo, 2023. "Partially identifying competing risks models: An application to the war on cancer," Journal of Econometrics, Elsevier, vol. 234(2), pages 536-564.
    4. Mogens Fosgerau & Dennis Kristensen, 2021. "Identification of a class of index models: A topological approach," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 121-133.
    5. Fan, Yanqin & Liu, Ruixuan, 2018. "Partial identification and inference in censored quantile regression," Journal of Econometrics, Elsevier, vol. 206(1), pages 1-38.
    6. Dahl, Christian M. & Effraimidis, Georgios & Pedersen, Mikkel H., 2019. "Nonparametric wind power forecasting under fixed and random censoring," Energy Economics, Elsevier, vol. 84(C).
    7. Ã ureo 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.
    8. 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.
    9. Effraimidis, Georgios, 2016. "Nonparametric Identification of a Time-Varying Frailty Model," DaCHE discussion papers 2016:6, University of Southern Denmark, Dache - Danish Centre for Health Economics.
    10. Florian Gunsilius, 2018. "Point-identification in multivariate nonseparable triangular models," Papers 1806.09680, arXiv.org.
    11. Arthur Lewbel, 2019. "The Identification Zoo: Meanings of Identification in Econometrics," Journal of Economic Literature, American Economic Association, vol. 57(4), pages 835-903, December.

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