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Title: Band-pass filtering with high-dimensional time series Authors:  Alessandro Giovannelli - University of L'Aquila (Italy) [presenting]
Marco Lippi - Universita di Roma La Sapienza (Italy)
Tommaso Proietti - University of Roma Tor Vergata (Italy)
Abstract: The focus is on the construction of a synthetic indicator of economic growth obtained by projecting a measure of aggregate economic activity, such as gross domestic product (GDP), onto high-frequency smooth principal components representative of the medium-to-long-run component of growth in a high-dimensional time series. The smooth principal components result from applying a suitable cross-sectional filter. The result is a monthly nowcast of the medium-to-long-run component of GDP growth. After discussing the theoretical properties of the indicator, we deal with the assessment of its reliability and predictive validity with reference to US data.