Band-Pass Filtering with High-Dimensional Time Series
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.
Cite
@article{arxiv.2305.06618,
title = {Band-Pass Filtering with High-Dimensional Time Series},
author = {Alessandro Giovannelli and Marco Lippi and Tommaso Proietti},
journal= {arXiv preprint arXiv:2305.06618},
year = {2023}
}