Quantile Regression Random Effects
Quantile Regression Random Effects
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分位数回归随机效应
DOI：
10.2139/ssrn.2746894
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Alexandre Poirier
中科院分区：
文献类型：
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作者：
A. Galvao;Alexandre Poirier
建立了分位数回归的随机效应模型。我们建立识别的QR系数，并开发实用的估计和推理程序。我们采用一个简单的池QR估计估计的兴趣系数，并推导出其统计性质。随机效应诱导集群依赖，因此，我们使用集群鲁棒方差协方差矩阵估计的推断，并建立其一致的一致性分位数集。我们还开发了一种新的检验方法，用于QR模型中线性假设的一致性检验。该程序是一种修改的Wald检验，适用于越来越多的分位数，因此，渐近地，该检验在分位数上是均匀的。我们证明了这个过程可以应用于检验QR面板数据模型中的随机效应假设。我们的模型和固定效应QR模型之间的两个显着差异是可以估计时不变回归量的影响，并且时间序列维度可以是小的和有限的。我们提供Monte Carlo模拟来评估有限样本的估计和推断过程的性能。最后，我们应用所提出的方法来研究教育和能力在工资决定中的作用。我们的文件强烈的异质性回报教育沿着收入的条件分布。
This paper develops a random effects model for quantile regression (QR). We establish identification of the QR coefficients, and develop practical estimation and inference procedures. We employ a simple pooled QR estimator to estimate the coefficients of interest, and derive its statistical properties. The random effects induce cluster dependence hence we use a cluster-robust variance-covariance matrix estimator for inference, and establish its uniform consistency over the set of quantiles. We also develop a new test procedure for uniform testing of linear hypotheses in QR models. This procedure is a modified Wald test applied on a growing number of quantiles such that, asymptotically, the test is uniform over the quantiles. We show this procedure can be applied to test the random effects hypothesis in QR panel data models. Two significant differences between our model and fixed-effects QR models are that effects of time-invariant regressors can be estimated, and that the time-series dimension can be small and finite. We provide Monte Carlo simulations to evaluate the finite sample performance of the estimation and inference procedures. Finally, we apply the proposed methods to study the roles of education and ability in wage determination. We document strong heterogeneity in returns to education along the conditional distribution of earnings.