Autoregressive Modeling of Canadian Money and Income Data

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Abstract
A sequential procedure based on Akaike's final prediction-error criterion and Granger's concept of causality to fit multiple auto-regressions is suggested. The method not only allows each variable to enter the equation with a different time lag but also provides a reasonably powerful test of exogeneity or causality. The idea is applied to Canadian postwar money and income data. It is found that a bivariate feedback model for M1 and GNP and a one-way causal relation from GNP to M2 fit the data best. Diagnostic checks applied to our model seem to indicate the adequacy of our approach.

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