Abstract:
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Quantile treatment effects are often considered in a quantile regression model. In this study, we focus on the problem of testing whether the treatment effects are significant for a set of quantile levels (e.g., lower quantiles). We propose a rank-based test, which is a generalization of the rank score test in quantile regression at an individual quantile level. This test statistic allows us to quantify the treatment effect for a prespecified quantile interval by integrating the regression rank score against certain trimmed score function. A model-based bootstrap method is constructed to estimate the null distribution. A simulation study is conducted to demonstrate the validity and usefulness of the proposed test. We also apply our method to analyze the 2016 US birth weight data and S&P 500 index data.
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