nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2009‒10‒17
two papers chosen by
Philip Yu
Hong Kong University

  1. How to evaluate the impact of part-time sick leave on the probability of recovering By Andrén, Daniela
  2. Identification of a Heterogeneous Generalized Regression Model with Group Effects By Steven T. Berry; Philip A. Haile

  1. By: Andrén, Daniela (Department of Business, Economics, Statistics and Informatics)
    Abstract: This paper presents an econometric framework for analyzing part-time sick leave as a treatment method. We exemplify how the discrete choice one-factor model can address the importance of controlling for unobserved heterogeneity in understanding the selection into part-time/full-time sick leave and the probability to fully recover from a reduced work capacity. The results indicate that part-time sick listing increases the probability to recover compared to full-time sick listing when the expected time to recover is longer than 120 days.
    Keywords: part-time sick leave; discrete choice model; selection; unobserved heterogeneity.
    JEL: I12 J21 J28
    Date: 2009–10–13
  2. By: Steven T. Berry (Cowles Foundation, Yale University); Philip A. Haile (Cowles Foundation, Yale University)
    Abstract: We consider identification in a "generalized regression model" (Han, 1987) for panel settings in which each observation can be associated with a "group" whose members are subject to a common unobserved shock. Common examples of groups include markets, schools or cities. The model is fully nonparametric and allows for the endogeneity of group-specific observables, which might include prices, policies, and/or treatments. The model features heterogeneous responses to observables and unobservables, and arbitrary heteroskedasticity. We provide sufficient conditions for full identification of the model, as well as weaker conditions sufficient for identification of the latent group effects and the distribution of outcomes conditional on covariates and the group effect.
    Keywords: Nonparametric identification, Binary choice, Threshold crossing, Censored regression, Proportional hazard model
    JEL: C23 C24 C25
    Date: 2009–10

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