Nowcasting of Advance Estimates of Personal Consumption of Services in the U.S. National Accounts: Individual vs Forecasting Combination Approach

This paper evaluates two individual nowcasting frameworks, the bridge equation and bridging with factors model, in concert with a set of forecast combination techniques for nowcasting the advance estimates of quarterly personal consumption expenditures (PCE) of services at the detailed component level in the U.S. national accounts, using real time data from 2009Q3 to 2019Q4. We show that these individual nowcasting frameworks improve the accuracy of advance estimates of PCE services by reducing revisions in 74% of the components when quarterly source data become available. We also show that adding model-averaging techniques to nowcasting further improves the accuracy by reducing revisions in 91% of the detailed components. The model-averaging techniques considered in this study include simple averaging (mean, median, trimmed means), information-criterion-based averaging (AIC, BIC, log-likelihood averaging), Bates-Granger averaging with leave-one-out cross-validation errors, and covariance-minimization-based Jackknife and Mallows averaging. We evaluate the performances of all methods by comparing their root mean squared revisions (RMSR) in the advance estimates of each detailed component of PCE services. Our study demonstrates that nowcasting models and model-averaging techniques have the potential to be a powerful tool in reducing revisions in the early estimates in national economic account statistics at the detailed level.