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

Author

Listed:
  • Amadou Barry

    (Institut national de la recherche scientifique)

  • Karim Oualkacha

    (Université du Québec à Montréal)

  • Arthur Charpentier

    (Université du Québec à Montréal)

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 ).

Suggested Citation

  • Amadou Barry & Karim Oualkacha & Arthur Charpentier, 2023. "Alternative fixed-effects panel model using weighted asymmetric least squares regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 819-841, September.
    • Handle: RePEc:spr:stmapp:v:32:y:2023:i:3:d:10.1007_s10260-023-00692-3
    DOI: 10.1007/s10260-023-00692-3
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    References listed on IDEAS

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