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Interval censored regression with fixed effects.

Jason Abrevaya1, Chris Muris2

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Summary
This summary is machine-generated.

This study introduces new methods for estimating fixed-effects models with interval-censored data. The proposed estimators can directly assess causal effects, even when the exact dependent variable is unobserved.

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Area of Science:

  • Econometrics
  • Statistical Modeling

Background:

  • Many real-world datasets contain interval-censored dependent variables, where only the range of the variable is known.
  • Existing methods may struggle with identification and estimation in such scenarios, particularly with fixed effects.

Purpose of the Study:

  • To develop and evaluate methods for identifying and estimating fixed-effects models with interval-censored dependent variables.
  • To address both parametric (logistic errors) and semiparametric (unspecified error distribution) models.
  • To investigate the direct estimation of causal effects.

Main Methods:

  • Proposed a conditional-logit-type composite likelihood estimator for the parametric logistic fixed-effects model.
  • Developed a composite maximum-score-type estimator for the semiparametric model.
  • Allowed for heteroskedasticity across units and stationarity within units; the semiparametric model also accommodates serial correlation.

Main Results:

  • The proposed estimators identify the scale of coefficient parameters, enabling direct estimation of causal effects.
  • Monte Carlo simulations demonstrated the performance of the parametric estimator.
  • An empirical application to birthweight outcomes validated the practical utility of the parametric approach.

Conclusions:

  • The developed estimators provide a robust framework for analyzing interval-censored data in fixed-effects models.
  • Direct estimation of causal effects is achievable, enhancing the interpretability of results.
  • The methods are applicable in various fields, including health economics and social sciences.