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Inference on quantile processes with a finite number of clusters

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  • Hagemann, Andreas

Abstract

I introduce a generic method for inference on entire quantile and regression quantile processes in the presence of a finite number of large and arbitrarily heterogeneous clusters. The method asymptotically controls size by generating statistics that exhibit enough distributional symmetry such that randomization tests can be applied. The randomization test does not require ex-ante matching of clusters, is free of user-chosen parameters, and performs well at conventional significance levels with as few as five clusters. The method tests standard (non-sharp) hypotheses and can even be asymptotically similar in empirically relevant situations. The main focus of the paper is inference on quantile treatment effects but the method applies more broadly. Numerical and empirical examples are provided.

Suggested Citation

  • Hagemann, Andreas, 2025. "Inference on quantile processes with a finite number of clusters," Journal of Econometrics, Elsevier, vol. 249(PA).
    • Handle: RePEc:eee:econom:v:249:y:2025:i:pa:s0304407624000186
    DOI: 10.1016/j.jeconom.2024.105672
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    More about this item

    Keywords

    Cluster-robust inference; Quantiles; Treatment effects; Randomization inference; Difference in differences;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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