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Misspecified Model Estimation and Its Impact on Predictions

Author

Listed:
  • Junnan He
  • Lin Hu
  • Matthew Kovach
  • Anqi Li

Abstract

We study a linear statistical model where outcomes depend on regressors with fixed population coefficients and observation-specific latent coefficients, along with measurement errors. A decision-maker estimates population coefficients and uses the estimates to predict the latent coefficients for a given observation. We analyze how misspecification of some population coefficients distorts predictions, investigating comparative statics with respect to: (1) residual information in regressors associated with misspecified coefficients after projecting out those associated with free coefficients, (2) alignment between misspecification vector and latent-to-coefficient mapping. Applications include employee rating with unconscious bias and LLM-mediated consumer research.

Suggested Citation

  • Junnan He & Lin Hu & Matthew Kovach & Anqi Li, 2023. "Misspecified Model Estimation and Its Impact on Predictions," Papers 2309.08740, arXiv.org, revised Apr 2026.
    • Handle: RePEc:arx:papers:2309.08740
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    References listed on IDEAS

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    1. Baum, Matthew A. & Gussin, Phil, 2008. "In the Eye of the Beholder: How Information Shortcuts Shape Individual Perceptions of Bias in the Media," Quarterly Journal of Political Science, now publishers, vol. 3(1), pages 1-31, March.
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    3. Mira Frick & Ryota Iijima & Yuhta Ishii, 2022. "Dispersed Behavior and Perceptions in Assortative Societies," American Economic Review, American Economic Association, vol. 112(9), pages 3063-3105, September.
    4. Esponda, Ignacio & Pouzo, Demian & Yamamoto, Yuichi, 2021. "Asymptotic behavior of Bayesian learners with misspecified models," Journal of Economic Theory, Elsevier, vol. 195(C).
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