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Title: A class of periodic trend models for economic time series Authors:  Gian Luigi Mazzi - Independent Expert (Luxembourg) [presenting]
Tommaso Proietti - University of Roma Tor Vergata (Italy)
Martyna Marczak - University of Hohenheim (Germany)
Abstract: Trend and seasonality are the most prominent features of economic time series that are observed at the sub-annual frequency. Modelling these components serves a variety of analytical purposes, including seasonal adjustment and forecasting. We introduce unobserved components models for which both the trend and seasonal components arise from systematically sampling a multivariate transition equation, according to which each season evolves as a random walk with a drift. By modelling the disturbance covariance matrix we can encompass traditional models for seasonal time series, like the basic structural model, and can formulate more elaborate ones, dealing with time domain season specific features, such as seasonal heterogeneity and correlation, along with frequency domain features, related to the different role played like by the non-stationary cycles defined at the fundamental and the harmonic frequencies, in determining the shape of the seasonal pattern.