Abstract
An approximate dynamic factor model can substantially improve the reliability of real time output gap estimates. The model extracts a common component from macroeconomic indicators, which reduces errors in the gap due to data revisions. The model’s ability to handle the unbalanced arrival of data, also yields favorable nowcasting properties and thus starting conditions for the filtering of data into trend and deviations from trend. Combined with the method of augmenting data with forecasts prior to filtering, this greatly reduces the end-of-sample imprecision in the gap estimate. The increased precision has economic significance for real time policy decisions.