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Activity Number: 642
Type: Topic Contributed
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #320612 View Presentation
Title: A Class of Periodic Trend Models for Economic Time Series
Author(s): Gian Luigi Mazzi* and Tommaso Proietti and Martyna Marczak
Companies: European Commission - Eurostat and Universita di Roma Tor Vergata and University of Hohenheim
Keywords: Seasonal Cycles ; Seasonal Heterogeneity ; Unobserved Components ; Circular Correlation

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 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.

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