Forecasting inflation and output growth with credit‐card‐augmented Divisia monetary aggregates

Abstract

This paper investigates the performance of the Credit-Card-Augmented Divisia monetary aggregates in forecasting U.S. inflation and output growth at the 12-month horizon. We compute recursive and rolling out-of-sample forecasts using an Autoregressive Distributed Lag (ADL) model based on Divisia monetary aggregates. We use the three available versions of those monetary aggregate indices, including the original Divisia aggregates, the credit cardaugmented Divisia, and the credit-card-augmented Divisia inside money aggregates. The source of each is the Center for Financial Stability (CFS). We find that the smallest Root Mean Square Forecast Errors (RMSFE) are attained with the credit-card-augmented Divisia indices used as the forecast indicators. We also consider Bayesian vector autoregression (BVAR) for forecasting annual inflation and output growth.

Do Divisia monetary aggregates help forecast exchange rates in a negative interest rate environment?

“…In a similar vein, Liu and Serletis (2020) argue that unlike Divisia M4 aggregates, credit card-augmented Divisia M4 volatility negatively impacts economic activity. Barnett and Park (2021) use credit card-augmented Divisia monetary aggregates and credit card-augmented inside Divisia aggregates to forecast inflation.…”

The credit-augmented Divisia aggregates and the monetary business cycle

“…In this regard, also find that the volatility of the credit card-augmented (broad) Divisia M4 monetary aggregate has a statistically significant negative impact on output whereas there is no effect of the conventional Divisia M4 growth volatility on output. More recently, Barnett and Park (2023a), by using an autoregressive distributed lag model and Bayesian VAR, find that credit-augmented Divisia monetary aggregates are the better indicators for forecasting inflation and output. Also, Barnett and Park (2023b), in the context of a sign-restricted Bayesian VAR, find that considering credit-related variables and shocks helps to interpret recent economic phenomena, with the credit card-augmented Divisia aggregates being especially informative.…”

The Credit‐Card‐Services Augmented Divisia Monetary Aggregates*

“…. For example,Barnett and Park (2023) have explored the abilities of our structural credit-card augmented aggregates in forecasting inflation and output growth using an autoregressive distributed lag model.…”