Nowcasting Nominal GDP with the Credit-Card Augmented Divisia Monetary Aggregates

While credit cards provide transactions services, as do currency and demand deposits, credit cards have never been included in measures of the money supply. The reason is accounting conventions, which do not permit adding liabilities, such as credit card balances, to assets, such as money. However, economic aggregation theory and index number theory measure service flows and are based on microeconomic theory, not accounting. We derive theory needed to measure the joint services of credit cards and money. Carried forward rotating balances are not included in the current period weakly separable block, since they were used for transactions services in prior periods. The theory is developed for the representative consumer, who pays interest for the services of credit cards during the period used for transactions. This interest rate is reported by the Federal Reserve as the average over all credit card accounts, including those not paying interest. Based on our derived theory, we propose an empirical measurement of the joint services of credit cards and money. These new Divisia monetary aggregates are widely relevant to macroeconomic research.1 We evaluate the ability of our money aggregate measures to nowcast nominal GDP. This is currently topical, given proposals for nominal GDP targeting, which require monthly measures of nominal GDP. The nowcasts are estimated using only real time information, as available for policy makers at the time predictions are made. We use a multivariate state space model that takes into account asynchronous information inflow, as proposed in Barnett, Chauvet, and Leiva-Leon (2016). The model considers real time information that arrives at different frequencies and asynchronously, in addition to mixed frequencies, missing data, and ragged edges. The results indicate that the proposed parsimonious model, containing information on real economic activity, inflation, and the new augmented Divisia monetary aggregates, produces the most accurate real time nowcasts of nominal GDP growth. In particular, we find that inclusion of the new aggregate in our nowcasting model yields substantially smaller mean squared errors than inclusion of the previous Divisia monetary aggregates.