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Title: Periodic features and seasonality in high-frequency data Authors:  Tommaso Proietti - University of Roma Tor Vergata (Italy) [presenting]
Diego Pedregal - University of Castilla-La Mancha (Spain)
Abstract: The advances in information technology and survey methods have increased the availability of intra-daily, daily, and weekly time series. The availability of time series observed at a high frequency, such as weekly or daily, poses new challenges that have not been properly handled in the literature. High-frequency data are relevant for producing more timely and more temporally disaggregate estimates of economic signals. However, they suffer from noise contamination, and new seasonal components are introduced. Distilling the relevant economic signals in such an environment requires the ability to handle seasonality and outliers. Robust filtering methods for preprocessing data may be required. The presentation aims at reviewing the solutions that have been provided by the literature and at exposing some of the challenges open to further research. In particular, it focuses on parametric and semiparametric models of seasonality within an unobserved components framework, where the seasonal component is estimated along with other components.