Predictability of Aggregated Time Series

Macroeconomic series are often aggregated from higher-frequency data. We show that this seemingly innocent feature has far-reaching consequences for the predictability of such series. First, the series are predictable by construction. Second, conventional tests of predictability are less informative about the data-generating process than frequently assumed. Third, a simple improvement to the conventional test leads to a sizeable correction, making it necessary to re-evaluate existing forecasting approaches. Fourth, forecasting models should be estimated with end-of-period observations even when the goal is to forecast the aggregated series. We highlight the relevance of these insights for forecasts of several macroeconomic variables.