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Interval censored regression with fixed effects

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  • Jason Abrevaya
  • Chris Muris

Abstract

This paper considers identification and estimation of a fixed‐effects model with an interval‐censored dependent variable. In each time period, the researcher observes the interval (with known endpoints) in which the dependent variable lies but not the value of the dependent variable itself. Two versions of the model are considered: a parametric model with logistic errors and a semiparametric model with errors having an unspecified distribution. In both cases, the error disturbances can be heteroskedastic over cross‐sectional units as long as they are stationary within a cross‐sectional unit; the semiparametric model also allows for serial correlation of the error disturbances. A conditional‐logit‐type composite likelihood estimator is proposed for the logistic fixed‐effects model, and a composite maximum‐score‐type estimator is proposed for the semiparametric model. In general, the scale of the coefficient parameters is identified by these estimators, meaning that the causal effects of interest are estimated directly in cases where the latent dependent variable is of primary interest (e.g., pure data‐coding situations). Monte Carlo simulations and an empirical application to birthweight outcomes illustrate the performance of the parametric estimator.

Suggested Citation

  • Jason Abrevaya & Chris Muris, 2020. "Interval censored regression with fixed effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 198-216, March.
  • Handle: RePEc:wly:japmet:v:35:y:2020:i:2:p:198-216
    DOI: 10.1002/jae.2737
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    Cited by:

    1. Botosaru, Irene & Muris, Chris & Pendakur, Krishna, 2023. "Identification of time-varying transformation models with fixed effects, with an application to unobserved heterogeneity in resource shares," Journal of Econometrics, Elsevier, vol. 232(2), pages 576-597.
    2. Felix Chan & Laszlo Matyas & Agoston Reguly, 2024. "Modelling with Discretized Variables," Papers 2403.15220, arXiv.org.
    3. Irene Botosaru & Chris Muris & Krishna Pendakur, 2020. "Intertemporal Collective Household Models: Identification in Short Panels with Unobserved Heterogeneity in Resource Shares," CeMMAP working papers CWP26/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Bo E. Honor'e & Martin Weidner, 2020. "Moment Conditions for Dynamic Panel Logit Models with Fixed Effects," Papers 2005.05942, arXiv.org, revised Dec 2023.
    5. Irene Botosaru & Chris Muris, 2022. "Identification of time-varying counterfactual parameters in nonlinear panel models," Papers 2212.09193, arXiv.org, revised Nov 2023.

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