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The nonparametric identification of treatment effects in duration models

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)1491-1517
Number of pages27
Issue number5
DatePublished - 2003


This paper analyzes the specification and identification of causal multivariate duration models. We focus on the case in which one duration concerns the point in time a treatment is initiated and we are interested in the effect of this treatment on some outcome duration. We define "no anticipation of treatment" and relate it to a common assumption in biostatistics. We show that (i) no anticipation and (ii) randomized treatment assignment can be imposed without restricting the observational data. We impose (i) but not (ii) and prove identification of models that impose some structure. We allow for dependent unobserved heterogeneity and we do not exploit exclusion restrictions on covariates. We provide results for both single-spell and multiple-spell data. The timing of events conveys useful information on the treatment effect.

    Research areas

  • Anticipation, Bivariate duration analysis, Hazard rate, Partial likelihood, Program evaluation, Selectivity bias, Unobserved heterogeneity


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