Quantile regression-based seasonal adjustment

We introduce a seasonal adjustment method based on quantile regression that focuses on capturing different forms of deterministic seasonal patterns. Given a variable of interest, by describing its seasonal behaviour over an approximation of the entire conditional distribution, we are capable of removing seasonal patterns affecting the mean and/or the variance or seasonal patterns varying over quantiles of the conditional distribution. We provide empirical examples based on simulated and real data through which we compare our proposal to least squares approaches.