Seasonality in High Frequency Time Series

33 Pages Posted: 12 Mar 2021

See all articles by Tommaso Proietti

Tommaso Proietti

University of Rome II - Department of Economics and Finance

Diego J. Pedregal

University of Castilla-La Mancha

Date Written: March 11, 2021

Abstract

Time series observed at higher frequencies than monthly frequency display complex seasonal patterns that result from the combination of multiple seasonal patterns (with annual, monthly, weekly and daily periodicities) and varying periods, due to the irregularity of the calendar. The paper deals with modelling seasonality in high frequency data from two main perspectives: the stochastic harmonic approach, based on the Fourier representation of a periodic function, and the time-domain random effects approach. An encompassing representation illustrates the conditions under which they are equivalent. Three major challenges are considered: the first deals with modelling the effect of moving festivals, holidays and other breaks due to the calendar. Secondly, robust estimation and filtering methods are needed to tackle the level of outlier contamination, which is typically high, due to the lower level of temporal aggregation and the raw nature of the data. Finally, we focus on model selection strategies, which are important, as the number of harmonic or random components that are needed to account for the complexity of seasonality can be very large.

Keywords: State Space Models. Robust filtering. Seasonal Adjustment. Variable selection

JEL Classification: C22, C52, C58

Suggested Citation

Proietti, Tommaso and Pedregal, Diego J., Seasonality in High Frequency Time Series (March 11, 2021). CEIS Working Paper No. 508, Available at SSRN: https://ssrn.com/abstract=3802611 or http://dx.doi.org/10.2139/ssrn.3802611

Tommaso Proietti (Contact Author)

University of Rome II - Department of Economics and Finance ( email )

Via Columbia, 2
Rome, 00133
Italy

Diego J. Pedregal

University of Castilla-La Mancha ( email )

Plaza Universidad, 1
Cuenca 02071
Spain

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