Nowcasting with Mixed Frequency Principal Components When Not All Months Are the Same

This paper develops algorithms for estimating a principal components (PC) model for monthly macroeconomic data mixed with either weekly or daily data. The algorithms’ utilization of high frequency data allows it to account for differences in the temporal aggregation patterns among the monthly series that are simple aggregates or averages and those that have an alternative aggregation pattern such as data from the monthly employment report where the aggregation pattern is often series- and month-specific and depends on the timing of the twelfth day of the month. The algorithms are applied to three respective models with mixed frequency price related, labor market related, or economic activity related data, the latter of which is similar to the factor model used by the Atlanta Fed's GDPNow nowcasting model to forecast GDP source data. When aggregated appropriately, the mixed frequency activity model PC closely resembles both the Chicago Fed National Activity Index and GDPNow's dynamic factor. Moreover, the PCs from all three models can be well approximated using the subset of data that would remain available during a widespread government shutdown.