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Tommaso Proietti, Universitat di Roma Print
Thursday, 19 April 2018, 12:15 - 13:15

Tommaso Proietti, Universitat di Roma

A Class of Periodic Trend Models fro Seasonal Time Series

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. In this paper 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 di erent role played like by the nonstationary cycles defined at the fundamental and the harmonic frequencies, in determining the shape of the seasonal pattern.