Forecasting the Money Supply in Time Series Models
In this paper, time series techniques are used to forecast quarterly money supply levels. Results indicate that a bivariate model including an interest rate and M-1 predicts M-1 better than the univariate model using M-1 only and as well as a 5-variable model which adds prices, output, and credit.
The paper also presents evidence on the issue of using seasonally adjusted data in forecasting with time series models. The implications of these results apply to all econometric modeling. Results support the hypothesis that using seasonally adjusted data can lead to spurious correlation in multivariate models.
Working Papers of the Federal Reserve Bank of Cleveland are preliminary materials circulated to stimulate discussion and critical comment on research in progress. They may not have been subject to the formal editorial review accorded official Federal Reserve Bank of Cleveland publications. The views expressed in this paper are those of the authors and do not represent the views of the Federal Reserve Bank of Cleveland or the Federal Reserve System.
Suggested Citation
Bagshaw, Michael L., and William T. Gavin. 1983. “Forecasting the Money Supply in Time Series Models.” Federal Reserve Bank of Cleveland, Working Paper No. 83-04.
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