The Real-Time Predictive Content of Money for Output

34 Pages Posted: 13 Dec 2005

See all articles by Jeffery D. Amato

Jeffery D. Amato

Goldman Sachs International

Norman R. Swanson

Rutgers University - Department of Economics

Date Written: December 2000

Abstract

Data on monetary aggregates are subject to periodic redefinitions, presumably in part to improve their link to measures of output. Money data are also revised on a regular basis. Taking these data imperfections into account, we reassess the evidence on the marginal predictive content of M1 and m2 for real and nominal output. In particular, by first using the latest version of the data that is available, and then using sequences of historical time series that would have been available to forecasters in real-time, we are able to provide a comprehensive assessment of whether money is useful for predicting output. We conclude that the generally significant marginal predictive content of M1 and m2 for output that is found using a recently revised data set is not duplicated in a real-time setting, although M2 is shown to remain useful when 1-year ahead forecasts are constructed using fitted vector autoregressive models.

Suggested Citation

Amato, Jeffery D. and Swanson, Norman Rasmus, The Real-Time Predictive Content of Money for Output (December 2000). BIS Working Paper No. 96. Available at SSRN: https://ssrn.com/abstract=849064 or http://dx.doi.org/10.2139/ssrn.849064

Jeffery D. Amato (Contact Author)

Goldman Sachs International ( email )

United States

Norman Rasmus Swanson

Rutgers University - Department of Economics ( email )

NJ
United States

HOME PAGE: http://econweb.rutgers.edu/nswanson/

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