G. William Schwert

University of Rochester, Rochester, NY 14627
and National Bureau of Economic Research

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Carnegie-Rochester Conference Series on Public Policy, 10 (Spring 1979) 55-96

Reprinted in Theory, Policy, Institutions: Papers from the Carnegie-Rochester Conferences on Public Policy,
Karl Brunner and Allan Meltzer, eds. (Amsterdam: North-Holland, 1983) 215-256.

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This paper describes new time series techniques and shows the advantages and disadvantages of these techniques compared with more traditional methods. Cross-correlation analysis of residuals from autoregressive-integrated-moving average (ARIMA) models to determine the predictive relations between two variables. I conclude that it is important to consider the power of this procedure before putting much faith in empirical results that seem to find a "lack of relationship" between economic variables.

Key words: Causality tests, ARIMA

JEL Classifications: C22

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Cited 58 times in the SSCI through 2007

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© Copyright 1979, Elsevier

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