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.

翻译：本文探讨了一种经济增长综合指标的构建方法，通过将季度经济总量指标（即国内生产总值，GDP）投影到由有限个平滑主成分构成的空间中，这些主成分代表了月度频率高维时间序列中经济增长的中长期成分。平滑主成分通过应用截面滤波器实现，该滤波器可实时提取经济增长的低频成分。投影结果得到的是GDP增长中长期成分的月度即时预测。在讨论该指标的理论性质后，本文基于美国宏观经济时间序列面板数据，对其可靠性和预测有效性进行了评估。