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arXiv · 2305.06618

Band-Pass Filtering with High-Dimensional Time Series

Alessandro Giovannelli·Marco Lippi·Tommaso Proietti
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Abstract

The paper deals with the construction of a synthetic indicator of economic growth, obtained by projecting a quarterly measure of aggregate economic activity, namely gross domestic product (GDP), into the space spanned by a finite number of smooth principal components, representative of the medium-to-long-run component of economic growth of a high-dimensional time series, available at the monthly frequency. The smooth principal components result from applying a cross-sectional filter distilling the low-pass component of growth in real time. The outcome of the projection 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 a panel of macroeconomic U.S. time series.

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Alessandro Giovannelli, Marco Lippi, Tommaso Proietti. 2023-05-11. Band-Pass Filtering with High-Dimensional Time Series. https://arxiv.org/abs/2305.06618

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