Working Paper: CEPR ID: DP14100
Authors: Kfir Eliaz; Ran Spiegler; Yair Weiss
Abstract: To what extent can misspecified models generate false estimated correlations? We focus on models that take the form of a recursive system of linear regression equations. Each equation is fitted to minimize the sum of squared errors against an arbitrarily large sample. We characterize the maximal pairwise correlation that this procedure can predict given a generic objective covariance matrix, subject to the constraint that the estimated model does not distort the mean and variance of individual variables. We show that as the number of variables in the model grows, the false pairwise correlation can become arbitrarily close to one, regardless of the true correlation.
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JEL Codes: No JEL codes provided
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| X2 (C39) | (X1, X3) (C29) |
| X1 (Y60) | Y (Y10) |
| (X1, X3) (C29) | estimated correlation (C10) |
| model complexity (C52) | estimated correlation (C10) |