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Fused LASSO as Non-crossing Quantile Regression

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Listed:
  • Szendrei, Tibor

    (National Institute of Economic and Social Research (NIESR))

  • Bhattacharjee, Arnab

    (Heriot-Watt University, Edinburgh)

  • Schaffer, Mark E

    (Heriot-Watt University, Edinburgh)

Abstract

Quantile crossing has been a challenge for quantile regression, leading to research in how to obtain monotonically increasing quantile estimates. While important contributions, these papers do not provide insight into how enforcing monotonicity influences the estimated coefficients. This paper fills this gap and shows that non-crossing constraints are a type of fused-shrinkage. The proposed estimator has good fit and (fused) variable selection properties: it can reliably identify quantile varying parameters. We investigate the 'heat-or-eat' dilemma and show that prepayment has a non-linear impact on households' consumption choices. In a growth-at-risk application the estimator has the best forecast performance.

Suggested Citation

  • Szendrei, Tibor & Bhattacharjee, Arnab & Schaffer, Mark E, 2024. "Fused LASSO as Non-crossing Quantile Regression," IZA Discussion Papers 17149, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp17149
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    References listed on IDEAS

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    1. Szendrei, Tibor & Varga, Katalin, 2023. "Revisiting vulnerable growth in the Euro Area: Identifying the role of financial conditions in the distribution," Economics Letters, Elsevier, vol. 223(C).
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    8. V. Chernozhukov & I. Fernández-Val & A. Galichon, 2009. "Improving point and interval estimators of monotone functions by rearrangement," Biometrika, Biometrika Trust, vol. 96(3), pages 559-575.
    9. Tilmann Gneiting & Roopesh Ranjan, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 411-422, July.
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    More about this item

    Keywords

    fused-shrinkage; quantile regression; non-crossing constraints; LASSO;
    All these keywords.

    JEL classification:

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

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