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

Amadou Barry (), Karim Oualkacha () and Arthur Charpentier ()
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Amadou Barry: Institut national de la recherche scientifique
Karim Oualkacha: Université du Québec à Montréal
Arthur Charpentier: Université du Québec à Montréal

Statistical Methods & Applications, 2023, vol. 32, issue 3, No 5, 819-841

Abstract: 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 ).

Keywords: Expectile regression; Quantile regression; Fixed effects; Within-transformation; Endogenous model; Panel data. (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10260-023-00692-3

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