  The fixed-effects model estimates the regressor effects on the mean of the
response, which is inadequate to summarize the variable relationships in the
presence of heteroscedasticity. In this paper, we adapt the asymmetric least
squares (expectile) regression to the fixed-effects model and propose a new
model: expectile regression with fixed-effects $(\ERFE).$ The $\ERFE$ model
applies the within transformation strategy to concentrate out the incidental
parameter 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, \url{github.com/AmBarry/erfe}).
