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Smooth expectiles for panel data using penalized splines

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Abstract

Expectile regression is a topic which became popular in the last years. It includes ordinary mean regression as special case but is more general as it offers the possibility to also model non-central parts of a distribution. Semi-parametric expectile models have recently been developed and it is easy to perform flexible expectile estimation with modern software like R. We extend the model class by allowing for panel observations, i.e. clustered data with repeated measurements taken at the same individual. A random (individual) effect is incorporated in the model which accounts for the dependence structure in the data. We fit expectile sheets, meaning that not a single expectile is estimated but a whole range of expectiles is estimated simultaneously. The presented model allows for multiple covariates, where a semi-parametric approach with penalized splines is pursued to fit smooth expectile curves. We apply our methods to panel data from the German Socio-Economic Panel.

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Acknowledgments

Financial support from the German Research Foundation (DFG) grant KA 1188/7-1 is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Statistics, Ludwig Maximilian University of Munich, Ludwigstraße 33, 80539, Munich, Germany

    Linda Schulze Waltrup & Göran Kauermann

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  1. Linda Schulze Waltrup
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  2. Göran Kauermann
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Correspondence to Göran Kauermann.

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Schulze Waltrup, L., Kauermann, G. Smooth expectiles for panel data using penalized splines. Stat Comput 27, 271–282 (2017). https://doi.org/10.1007/s11222-015-9621-2

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  • DOI: https://doi.org/10.1007/s11222-015-9621-2

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