This study evaluates the information content of 25 measures of credit with respect to three macroeconomic variables—nominal spending, real spending and prices. Initially, simple descriptive techniques are used to assess the contemporaneous and leading relationships between the credit aggregates and the three goal variables. Next, bivariate vector autoregression models are constructed by regressing each of the goal variables on its own past values, then adding contemporaneous and lagged values of the credit aggregates. Multivariate models are developed by introducing other financial variables (which include an interest rate, an exchange rate and a stock market price index) into the simple bivariate equations as explanatory variables. The models using different measures of credit are compared with one another and with models using various monetary aggregates. It is found that, while individual monetary aggregates are more informative than measures of credit, the latter may have a valuable complementary role to play as macroeconomic indicators.