期刊
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
卷 41, 期 3, 页码 720-736出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/07350015.2022.2061495
关键词
Panel data; Quantile regression; Treatment effect
This study develops a novel estimation method for quantile treatment effects (QTE) and applies it to estimate the distributional effects of insurance provision on household production and TV watching on child cognitive development.
In this study, we develop a novel estimation method for quantile treatment effects (QTE) under rank invariance and rank stationarity assumptions. Ishihara (2020) explores identification of the nonseparable panel data model under these assumptions and proposes a parametric estimation based on the minimum distance method. However, when the dimensionality of the covariates is large, the minimum distance estimation using this process is computationally demanding. To overcome this problem, we propose a two-step estimation method based on the quantile regression and minimum distance methods. We then show the uniform asymptotic properties of our estimator and the validity of the nonparametric bootstrap. The Monte Carlo studies indicate that our estimator performs well in finite samples. Finally, we present two empirical illustrations, to estimate the distributional effects of insurance provision on household production and TV watching on child cognitive development.
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