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Treatment effect estimators for count data models
Author(s) -
Hasebe Takuya
Publication year - 2018
Publication title -
health economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.55
H-Index - 109
eISSN - 1099-1050
pISSN - 1057-9230
DOI - 10.1002/hec.3790
Subject(s) - estimator , econometrics , statistics , average treatment effect , outcome (game theory) , count data , regression , log normal distribution , treatment effect , economics , mathematics , medicine , mathematical economics , poisson distribution , traditional medicine
In this paper, we consider a switching regression model with count data outcomes, where the possible outcome differs across two alternate states and individuals endogenously select one of the states. We assume lognormal latent heterogeneity. Building on the switching regression model, we derive estimators of various treatment effects: the average treatment effect, the average treatment effect on the treated, the local average treatment effect, and the marginal treatment effect. We illustrate an application that examines the effects of public insurance on the number of doctor visits using the data employed by previous studies.