Constructing High Frequency Economic Indicators by Imputation

TL;DR

This paper develops methods to impute high frequency economic indicators from low frequency data using factor models, improving accuracy over static methods and revealing hidden economic variability.

Contribution

It introduces dynamic imputation procedures that account for serial correlation, enhancing the estimation of high frequency economic indicators from low frequency data.

Findings

Dynamic procedures outperform static matrix completion in imputation accuracy.

Imputed weekly indicators reveal episodes of economic variability masked in monthly data.

Method applied successfully to consumer sentiment and CFNAI indices.

Abstract

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.