  Monthly and weekly economic indicators are often taken to be the largest
common factor estimated from high and low frequency data, either separately or
jointly. To incorporate mixed frequency information without directly modeling
them, we target a low frequency diffusion index that is already available, and
treat high frequency values as missing. We impute these values using multiple
factors estimated from the high frequency data. In the empirical examples
considered, static matrix completion that does not account for serial
correlation in the idiosyncratic errors yields imprecise estimates of the
missing values irrespective of how the factors are estimated. Single equation
and systems-based dynamic procedures that account for serial correlation yield
imputed values that are closer to the observed low frequency ones. This is the
case in the counterfactual exercise that imputes the monthly values of consumer
sentiment series before 1978 when the data was released only on a quarterly
basis. This is also the case for a weekly version of the CFNAI index of
economic activity that is imputed using seasonally unadjusted data. The imputed
series reveals episodes of increased variability of weekly economic information
that are masked by the monthly data, notably around the 2014-15 collapse in oil
prices.
