This study assesses how logit, probit, and other parametric models handle hypotheses about variable interactions affecting event probabilities. Using Monte Carlo simulations, it finds that many standard models produce overly narrow confidence intervals for interaction strength estimates, leading to misplaced confidence in inaccurate results.
### Key Findings:
* Standard parametric methods (logit, probit) often generate point and interval estimates with significant average errors due to specification uncertainty.
* While some logit/probit variants offer more accurate interval estimation, inaccuracies remain prevalent across models.
### Why It Matters & Our Approach:
The paper argues that typical parametric modeling struggles under the common challenge of insufficient theoretical guidance for functional form. It proposes strategies to improve the use of these standard models in such situations but ultimately suggests nonparametric approaches might be superior.
| Bias and Overconfidence in Parametric Models of Interactive Processes was authored by William Berry, Jacqueline H.R. DeMeritt and Justin Esarey. It was published by Wiley in AJPS in 2016. |
