Constructive identification of the mixed proportional hazards model

Kortram, R.A.; Rooij, A. C. M. van; Lenstra, Andries; Ridder, Geert

doi:10.1111/j.1467-9574.1995.tb01469.x

Cited by 27 publications

(24 citation statements)

References 13 publications

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“…) are analytic on (0, L G (0)) (Kortram et al, 1995), this equality extends to (0, L G (0)). Iterating n times, this implies that…”

Section: Resultsmentioning

confidence: 96%

Mixed Hitting-Time Models

Abbring¹

2012

Econometrica

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We study a mixed hitting-time (MHT) model that specifies durations as the first time a Lévy process-a continuous-time process with stationary and independent increments-crosses a heterogeneous threshold. Such models are of substantial interest because they can be reduced from optimal-stopping models with heterogeneous agents that do not naturally produce a mixed proportional hazards (MPH) structure. We show how strategies for analyzing the MPH model's identifiability can be adapted to prove identifiability of an MHT model with observed regressors and unobserved heterogeneity. We discuss inference from censored data and extensions to time-varying covariates and latent processes with more general time and dependency structures. We conclude by discussing the relative merits of the MHT and MPH models as complementary frameworks for econometric duration analysis.

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“…) are analytic on (0, L G (0)) (Kortram et al, 1995), this equality extends to (0, L G (0)). Iterating n times, this implies that…”

Section: Resultsmentioning

confidence: 96%

Mixed Hitting-Time Models

Abbring¹

2012

Econometrica

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“…• Elbers and Ridder (1982) and Kortram et al (1995) Because H has no support outside (0, ∞), we also have that lim s→0 g(s) > 0.…”

Section: Sufficiency Of (Iii)mentioning

confidence: 99%

Regular Variation and the Identification of Generalized Accelerated Failure-Time Models

Abbring

Ridder

2011

SSRN Journal

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Ridder (1990) provides an identification result for the Generalized AcceleratedFailure-Time (GAFT) model. We point out that Ridder's proof of this result is incomplete, and provide an amended proof with an additional necessary and sufficient condition that requires that a function varies regularly at 0 and ∞. We also give more readily interpretable sufficient conditions on the tails of the error distribution or the asymptotic behavior of the transformation of the dependent variable. The sufficient conditions are shown to encompass all previous results on the identification of the Mixed Proportional Hazards (MPH) model. Thus, this paper not only clarifies, but also unifies the literature on the non-parametric identification of the GAFT and MPH models.

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“…Under the additional assumption that E [V ] < ∞ the parameter γ is identified from (see Elbers andRidder, 1982, andKortram et al, 1995),…”

Section: Semi-parametric Approachesmentioning

confidence: 99%

Social experiments and instrumental variables with duration outcomes

Abbring¹,

Berg²

2005

Working Paper Series

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This paper examines the empirical analysis of treatment effects on duration outcomes from data that contain instrumental variation. We focus on social experiments in which an intention to treat is randomized and compliance may be imperfect. We distinguish between cases where the treatment starts at the moment of randomization and cases where it starts at a later point in time. We derive exclusion restrictions under various informational and behavioral assumptions and we analyze identifiability under these restrictions. It turns out that randomization (and by implication, instrumental variation) by itself is often insufficient for inference on interesting effects, and needs to be augmented by a semi-parametric structure. We develop corresponding non-and semi-parametric tests and estimation methods.

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Constructive identification of the mixed proportional hazards model

Cited by 27 publications

References 13 publications

Mixed Hitting-Time Models

Mixed Hitting-Time Models

Regular Variation and the Identification of Generalized Accelerated Failure-Time Models

Social experiments and instrumental variables with duration outcomes

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