Approximating and forecasting macroeconomic signals in real-time

We incorporate factors extracted from a large panel of macroeconomic time series in the predictions of two signals related to real economic activity: business cycle fluctuations and the medium- to long-run component of output growth. The latter is simply output growth short of fluctuations with a period below one year. For forecasting purposes, we show that targeting this object rather than the original (noisy) time series can result in gains in forecast accuracy. With conventional projections, high-frequency fluctuations are always fitted, despite being (mostly) unpredictable or idiosyncratic. We illustrate the methodology and provide forecast comparisons for the U.S. and Portugal.