Tests of Causality
The Message in the Innovations
G. William Schwert
University of Rochester, Rochester, NY 14627
and National Bureau of Economic Research
Carnegie-Rochester Conference Series on Public Policy, 10 (Spring
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.
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
Cited 61 times in the SSCI and SCOPUS through 2020
© Copyright 1979, Elsevier
The following file contains the reprint of this paper in Acrobat's portable
data format (.pdf).
Click here to download
this paper in PDF format.
Return to Publications Page
© Copyright 1998-2021, G. William
Last Updated on 6/10/2021