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Author: Ishihara, Takuya
Resulting in 1 citation.
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Ishihara, Takuya |
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Panel Data Quantile Regression for Treatment Effect Models Journal of Business and Economic Statistics published online (1 April 2022): DOI: 10.1080/07350015.2022.2061495. Also: https://www.tandfonline.com/doi/full/10.1080/07350015.2022.2061495 Cohort(s): Children of the NLSY79 Publisher: American Statistical Association Keyword(s): Cognitive Development; Modeling; Monte Carlo; Peabody Individual Achievement Test (PIAT- Reading); Statistical Analysis; Television Viewing 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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Bibliography Citation
Ishihara, Takuya. "Panel Data Quantile Regression for Treatment Effect Models." Journal of Business and Economic Statistics published online (1 April 2022): DOI: 10.1080/07350015.2022.2061495.
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