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Related Experiment Videos

What Quantile Regression Does and Doesn't Do: A Commentary on Petscher and Logan (2014).

Sebastian E Wenz1

  • 1GESIS-Leibniz Institute for the Social Sciences.

Child Development
|September 30, 2018
PubMed
Summary

Quantile regression (QR) estimates the conditional quantile function, not the unconditional distribution. This study clarifies QR

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Inappropriate causal assumptions underlie Killingsworth, Kahneman, and Mellers' conclusions.

Proceedings of the National Academy of Sciences of the United States of America·2024
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Area of Science:

  • Statistics
  • Econometrics
  • Data Analysis

Background:

  • Petscher and Logan's (2014) description of quantile regression (QR) may lead to misconceptions.
  • Existing literature might incorrectly present QR as estimating unconditional quantiles.
  • Clarifying the distinction between conditional and unconditional quantile estimation is crucial for accurate statistical modeling.

Purpose of the Study:

  • To address potential methodological misconceptions in the presentation of quantile regression (QR).
  • To contrast the features of QR with linear regression to highlight accurate modeling approaches.
  • To emphasize the importance of a correct understanding of QR in empirical research.

Main Methods:

  • Comparative analysis of quantile regression and linear regression methodologies.

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  • Discussion of potential consequences stemming from methodological formulation errors.
  • Illustration of similarities and differences using simulated data across various QR estimators and linear regression.

    • Main Results:

    • Quantile regression models the conditional quantile function of an outcome variable given predictors.
    • This contrasts with linear regression, which models the conditional mean function.
    • Simulated data analysis reveals key differences and similarities between QR estimators and linear regression.

    Conclusions:

    • Accurate understanding of quantile regression is vital for correct application in empirical studies.
    • Misinterpretations can lead to flawed analytical outcomes.
    • This work reinforces the conditional nature of QR estimation, akin to the conditional mean in linear regression.