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Abstract:
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Signal extraction in economic data frequently entails removing seasonality from the data. This process is typically performed on an individual series basis, with little consideration for the interrelated nature of economic data. A multivariate approach permits more complex structure between series to be accommodated. In this work, we examine the application of multivariate signal extraction techniques to data from the U.S. Census Bureau’s Manufacturers’ Shipments, Inventories, and Orders (M3) survey. We compare the performance to those of univariate model-based signal extraction approaches. We focus on practical implications and discuss findings as they relate to moving from univariate to multivariate signal extraction. This broader overview may provide additional insight into the feasibility of adopting multivariate signal extraction procedures for use in production.
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