Forecasting Inflation and Output Growth with Credit-Card-Augmented Divisia Monetary Aggregates
William Barnett () and
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Sohee Park: Department of Economics, University of Kansas
No 202120, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics
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 card-augmented 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.
Keywords: Divisia; Credit-Card-Augmented Divisia; Monetary Aggregates; Forecasting; Bayesian vector autoregression; Inflation; Output Growth. (search for similar items in EconPapers)
JEL-codes: C32 C53 E31 E47 E51 (search for similar items in EconPapers)
Date: 2021-10, Revised 2021-10
New Economics Papers: this item is included in nep-cba, nep-fdg, nep-for, nep-mac, nep-mon and nep-pay
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Working Paper: Forecasting Inflation and Output Growth with Credit-Card-Augmented Divisia Monetary Aggregates (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:kan:wpaper:202120
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