  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.
