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Fused LASSO as Non-Crossing Quantile Regression

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Listed:
  • Tibor Szendrei
  • Arnab Bhattacharjee
  • Mark E. Schaffer

Abstract

Growth-at-Risk is vital for empirical macroeconomics but is often suspect to quantile crossing due to data limitations. While existing literature addresses this through post-processing of the fitted quantiles, these methods do not correct the estimated coefficients. We advocate for imposing non-crossing constraints during estimation and demonstrate their equivalence to fused LASSO with quantile-specific shrinkage parameters. By re-examining Growth-at-Risk through an interquantile shrinkage lens, we achieve improved left-tail forecasts and better identification of variables that drive quantile variation. We show that these improvements have ramifications for policy tools such as Expected Shortfall and Quantile Local Projections.

Suggested Citation

  • Tibor Szendrei & Arnab Bhattacharjee & Mark E. Schaffer, 2024. "Fused LASSO as Non-Crossing Quantile Regression," Papers 2403.14036, arXiv.org, revised Apr 2025.
  • Handle: RePEc:arx:papers:2403.14036
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    References listed on IDEAS

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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