Band-Pass Filtering with High-Dimensional Time Series
Giovannelli AlessandroLippi MarcoProietti Tommaso
CEIS Research Paper
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.
Keywords: Nowcasting, Principal Components Analysis, Macroeconomic Indicators
JEL codes: C22,C52,C58
Date: Thursday, June 15, 2023
Revision Date: Thursday, June 15, 2023