Interval censored regression with fixed effects
研究了固定效应模型中因变量为区间删失时的识别与估计，提出了适用于逻辑分布误差和半参数误差的两种估计量，并通过模拟和出生体重数据验证了参数估计量的表现。
Summary 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.