Spiegler, R;
Eliaz, K;
Weiss, Y;
(2021)
Cheating with models.
American Economic Review: Insights
(In press).
Preview |
Text
cheating.pdf - Accepted Version Download (208kB) | Preview |
Abstract
Beliefs and decisions are often based on confronting models with data. What is the largest "fake" correlation that a misspecified model can generate, even when it passes an elementary misspecification test? We study an "analyst" who fits a model, represented by a directed acyclic graph, to an objective (multivariate) Gaussian distribution. We characterize the maximal estimated pairwise correlation for generic Gaussian objective distributions, subject to the constraint that the estimated model preserves the marginal distribution of any individual variable. As the number of model variables grows, the estimated correlation can become arbitrarily close to one, regardless of the objective correlation.
| Type: | Article |
|---|---|
| Title: | Cheating with models |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://www.aeaweb.org/articles?id=10.1257/aeri.20... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10125322 |
Archive Staff Only
 |
View Item |
