Title: Quantile regression-based seasonal adjustment

Authors: Massimiliano Caporin; Mohammed Elseidi

Addresses: Department of Statistical Sciences, University of Padova, Via C. Battisti 241, Padova, Italy ' Department of Applied Statistics and Insurance, Faculty of Commerce, Mansoura University, El Gomhouria St., 35516, Mansoura, Egypt

Abstract: 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.

Keywords: quantile regression; seasonal adjustment; deterministic seasonal patterns.

DOI: 10.1504/IJCEE.2023.132144

International Journal of Computational Economics and Econometrics, 2023 Vol.13 No.3, pp.270 - 304

Received: 21 Oct 2021
Accepted: 01 Feb 2022
Published online: 12 Jul 2023 *

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