Nonparametric Identification of Accelerated Failure Time Competing Risks Models
AbstractWe 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.
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Bibliographic InfoPaper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number 755.
Date of creation: 01 Apr 2010
Date of revision: 30 Jun 2011
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accelerated failure time models; competing risks; identifiability.;
Other versions of this item:
- Lee, Sokbae & Lewbel, Arthur, 2013. "Nonparametric Identification Of Accelerated Failure Time Competing Risks Models," Econometric Theory, Cambridge University Press, vol. 29(05), pages 905-919, October.
- 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.
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