The Impossibility of Causality Testing

Causality tests developed by Sims and Granger are fatally flawed for several reasons First, when two variables, X and Y, are uncorrelated, X has no linear predictive value for Y, but X,and Y may be nonlinearly related unless they are statistically Independent, In which case X and Y are not related at all The light-hand side variables In a regression equation are exogenous If they are mean Independent of the disturbance term Mean Independence IS stronger than uncorrelatedness The proofs for deriving causality-exogenity tests Imply weaker results than statistical or mean Independence Second, transformations such as the Box-Cox transformation and Box Jenkins stationarity-inducing transformations are not causality preserving Third, counterexamples constructed by Price have invalidated the Pierce, Haugh theorem on Instantaneous causality Fourth, omission of other variables influencing those tested renders any test results Spurious Finally, causality tests are inconsistent because ,they are based on underidentified models We provide a logically valid method of building models which does not use causality tests


Issue Date:
1984
Publication Type:
Journal Article
PURL Identifier:
http://purl.umn.edu/149081
Published in:
Agricultural Economics Research, Volume 36, Number 3
Page range:
1-19
Total Pages:
19




 Record created 2017-04-01, last modified 2017-08-27

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