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Constructing High Frequency Economic Indicators by Imputation
\title{Constructing High Frequency Economic Indicators by Imputation}
\author{Serena Ng\footnote{Corresponding author: Department of Economics, Columbia University and NBER. Email: serena.ng at columbia.edu 420 W. 118 St. MC 3308, New York, NY 10027}
\and Susannah Scanlan\footnote{Department of Economics, Columbia University. \newline
We thank Siem Koopman, Jushan Bai, Joerg Breitung, Krishna Kamepalli, and Siem Koopmans for many helpful discussions and comments. This research is supported by the National Science Foundation (SES 2018369)}
}
\date{\today}
\maketitle
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\begin{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.
{\bf Keywords} Missing Data, Interpolation, Chow-Lin, Temporal disaggregation, Seasonality.
{\bf JEL Classification: C21, C24, C25}
\end{abstract}
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