Abstract: We compare five methods for the decomposition and seasonal adjustment of daily time series: TBATS, STR, DSA, AMB and X11plus. They cover a broad range of methods, ranging from structural or reduced form time series models to semi-parametric or algorithmic approaches. The comparison is made by means of a Monte Carlo experiment based on a standard structural time series model parameterized in different ways. The stochastic setting is later expanded to include two alternative exogenous variables: a deterministic end of month effect and an exogenous factor that mimics the COVID-19 shock. In this way, we can assess the relative performance of the methods under controlled conditions as well as their sensitivity to deviations from the standard model.

Discussants: Ángel Cuevas y Enrique Quilis.

Chair: Matías Pacce y Morteza Ghomi.

Contact: Matías Pacce.

Location: Meeting room DG Economics.

Timetable: 2025.06.04 (10:00-11:00).