Seasonality in High Frequency Time Series

Tommaso Proietti and Diego J. Pedregal ()
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Diego J. Pedregal: Universidad de Castilla-La Mancha

No 508, CEIS Research Paper from Tor Vergata University, CEIS

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 (search for similar items in EconPapers)
JEL-codes: C22 C52 C58 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2021-03-11, Revised 2021-03-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Journal Article: Seasonality in High Frequency Time Series (2023)
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