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Inference on Conditional Quantile Processes in Partially Linear Models with Applications to the Impact of Unemployment Benefits

Author

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  • Zhongjun Qu

    (Boston University)

  • Jungmo Yoon

    (Hanyang University)

  • Pierre Perron

    (Boston University)

Abstract

We propose methods to estimate and make inferences on conditional quantile processes for models with both nonparametric and (locally or globally) linear components. We derive their asymptotic properties, optimal bandwidths, and uniform confidence bands over quantiles allowing for robust bias correction. Our framework covers the sharp regression discontinuity design, which is used to study the effects of unemployment insurance benefits extensions, focusing on heterogeneity over quantiles and covariates. We show economically strong effects in the tails of the outcome distribution. They reduce the within-group inequality, but can be viewed as enhancing between-group inequality, although they help to bridge the gender gap.

Suggested Citation

  • Zhongjun Qu & Jungmo Yoon & Pierre Perron, 2024. "Inference on Conditional Quantile Processes in Partially Linear Models with Applications to the Impact of Unemployment Benefits," The Review of Economics and Statistics, MIT Press, vol. 106(2), pages 521-541, March.
    • Handle: RePEc:tpr:restat:v:106:y:2024:i:2:p:521-541
    DOI: 10.1162/rest_a_01168
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