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Quantile regression in Stata: Performance, precision, and power

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  • Morten Wang Fagerland

    (Oslo University Hospital)

Abstract

Quantile regression (command qreg) estimates quantiles of the outcome variable, conditional on the values of the independent variables, with median regression as the default form. Quantile regression can be used for several purposes: to estimate medians instead of means as a measure of central tendency—for instance, when data are markedly skewed; to estimate a particular quantile that may be of interest, such as the 10th quantile of birthweight to find predictors of low birthweight; or to study how the effects of independent variables vary over different quantiles of the dependent variable. Specifying the variance–covariance estimator for quantile regression is not straightforward. qreg offers both independent and identically distributed (i.i.d.) and robust estimators. The density estimation technique (DET) can be fitted, residual (i.i.d. only), or kernel. Three different bandwidth methods are available with the fitted and residual DETs, and eight kernel functions are available for the kernel DET. There is also a bootstrap option, which puts the total number of methods at 26. A natural question arises: which one to use? The aim of this presentation is to explore the performance of the methods and to arrive at some overall recommendations for which methods to use.

Suggested Citation

  • Morten Wang Fagerland, 2022. "Quantile regression in Stata: Performance, precision, and power," 2022 Stata Conference 02, Stata Users Group.
    • Handle: RePEc:boc:usug22:02
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