Nonparametric Identification of Accelerated Failure Time Competing Risks Models

Lee, Sokbae; Lewbel, Arthur

doi:10.1017/s0266466612000795

Cited by 24 publications

(16 citation statements)

References 34 publications

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“…Identification without exclusion restriction of the competing risk model, which is strongly related to the standard Roy model, has already been considered in the literature by Heckman & Honore (1989), 6 Abbring & van den Berg (2003), Lee (2006) and Lee & Lewbel (2008). 7 However, all of the strategies proposed in these papers break down when turning to generalized Roy models.…”

Section: Application To Generalized Roy Modelsmentioning

confidence: 99%

Another Look at the Identification at Infinity of Sample Selection Models

Maurel

2012

Econom. Theory

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Another Look at the Identification at Infinity ofSample Selection Models * It is often believed that without instrument, endogenous sample selection models are identified only if a covariate with a large support is available (see Chamberlain, 1986, andLewbel, 2007). We propose a new identification strategy mainly based on the condition that the selection variable becomes independent of the covariates when the outcome, not one of the covariates, tends to infinity. No large support on the covariates is required. Moreover, we prove that this condition is testable. We finally show that our strategy can also be applied to the identification of generalized Roy models.JEL Classification: C21

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Section: Application To Generalized Roy Modelsmentioning

confidence: 99%

Another Look at the Identification at Infinity of Sample Selection Models

Maurel

2012

Econom. Theory

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“…Identification results under various assumptions were established by Heckman and Honoré (), Sueyoshi (), Abbring and van den Berg (), and Lee and Lewbel (). In general, there have been three different approaches to identification (Honoré and Lleras‐Muney, ): (a) to make no additional assumptions beyond the latent CR structure and estimate bounds on the objects of interest; (b) assume that the risks are independent conditional on a set of observed covariates and deal with a multiple‐duration model environment; and (c) to specify a parametric or semiparametric model conditional on the covariates, Here we take the last approach.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Semiparametric Competing Risk Model with Unobserved Heterogeneity

Burda

Harding

Hausman

2014

J of Applied Econometrics

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This paper generalizes existing econometric models for censored competing risks by introducing a new flexible specification based on a piecewise linear baseline hazard, time‐varying regressors, and unobserved individual heterogeneity distributed as an infinite mixture of generalized inverse Gaussian (GIG) densities, nesting the gamma kernel as a special case. A common correlated latent time effect induces dependence among risks. Our model is based on underlying latent exit decisions in continuous time while only a time interval containing the exit time is observed, as is common in economic data. We do not make the simplifying assumption of discretizing exit decisions—our competing risk model setup allows for latent exit times of different risk types to be realized within the same time period. In this setting, we derive a tractable likelihood based on scaled GIG Laplace transforms and their higher‐order derivatives. We apply our approach to analyzing the determinants of unemployment duration with exits to jobs in the same industry or a different industry among unemployment insurance recipients on nationally representative individual‐level survey data from the US Department of Labor. Our approach allows us to conduct a counterfactual policy experiment by changing the replacement rate: we find that the impact of its change on the probability of exit from unemployment is inelastic. Copyright © 2014 John Wiley & Sons, Ltd.

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“…Finally, there is also a literature on identi…cation in competing risk models. The two most closely related papers in terms of modelling are Heckman and Honoré (1989) and Lee and Lewbel (2013). Heckman and Honoré (1989) achieves identi…cation by letting the index of the duration variable go to zero and so their result falls in the "thin set identi…cation" category.…”

Section: Introductionmentioning

confidence: 99%

“…Heckman and Honoré (1989) achieves identi…cation by letting the index of the duration variable go to zero and so their result falls in the "thin set identi…cation" category. Lee and Lewbel (2013) provide a high-level assumption for identi…cation involving a rank condition of an integral operator. Primitive conditions for this to hold are not known.…”

Section: Introductionmentioning

confidence: 99%

Identification of a class of index models: A topological approach

Fosgerau¹,

Kristensen²

2019

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. AbstractWe establish nonparametric identi…cation in a class of so-called index models using a novel approach that relies on general topological results.Our proof strategy imposes very weak smoothness conditions on the functions to be identi…ed and does not require any large support conditions on the regressors in our model. We apply the general identi…cation result to additive random utility and competing risk models.

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Nonparametric Identification of Accelerated Failure Time Competing Risks Models

Cited by 24 publications

References 34 publications

Another Look at the Identification at Infinity of Sample Selection Models

Another Look at the Identification at Infinity of Sample Selection Models

A Bayesian Semiparametric Competing Risk Model with Unobserved Heterogeneity

Identification of a class of index models: A topological approach

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