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Alternative fixed-effects panel model using weighted asymmetric least squares regression

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Abstract

A fixed-effects model estimates the regressor effects on the mean of the response, which is inadequate to account for heteroscedasticity. In this paper, we adapt the asymmetric least squares (expectile) regression to the fixed-effects panel model and propose a new model: expectile regression with fixed effects (ERFE). The ERFE model applies the within transformation strategy to solve the incidental parameter problem and estimates the regressor effects on the expectiles of the response distribution. The ERFE model captures the data heteroscedasticity and eliminates any bias resulting from the correlation between the regressors and the omitted factors. We derive the asymptotic properties of the ERFE estimators and suggest robust estimators of its covariance matrix. Our simulations show that the ERFE estimator is unbiased and outperforms its competitors. Our real data analysis shows its ability to capture data heteroscedasticity (see our R package, https://github.com/amadoudiogobarry/erfe).

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Funding

This work was funded by the Fonds de recherche du Québec—Société et culture (FRQSC) to Dr. Amadou Barry.

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Authors and Affiliations

  1. Centre Armand-Frappier Santé Biotechnologie, Institut national de la recherche scientifique, 531, Boulevard des Prairies, Laval, QC, H7V 1B7, Canada

    Amadou Barry

  2. Department of Mathematics and Statistics, Université du Québec à Montréal, 201, Avenue du Président-Kennedy, Montréal, QC, H2X 3Y7, Canada

    Karim Oualkacha & Arthur Charpentier

Authors
  1. Amadou Barry
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  2. Karim Oualkacha
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  3. Arthur Charpentier
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Correspondence to Amadou Barry.

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Barry, A., Oualkacha, K. & Charpentier, A. Alternative fixed-effects panel model using weighted asymmetric least squares regression. Stat Methods Appl 32, 819–841 (2023). https://doi.org/10.1007/s10260-023-00692-3

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  • DOI: https://doi.org/10.1007/s10260-023-00692-3

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