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Rounding the (Non)Bayesian Curve: Unraveling the Effects of Rounding Errors in Belief Updating

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  • James Bland
  • Yaroslav Rosokha

Abstract

Estimation of belief learning models relies on several important assumptions regarding measurement errors. Whereas existing work has focused on classical measurement errors, the current paper is the first to investigate the impact of a non-classical, behavioral measurement error—rounding bias. In particular, we design and carry out a novel economics experiment in conjunction with simulations and a meta-study of existing papers to show a strong impact of rounding bias on belief updating. In addition, we propose an econometric technique to aid researchers in overcoming challenges posed by the rounded responses in belief elicitation questions.

Suggested Citation

  • James Bland & Yaroslav Rosokha, 2024. "Rounding the (Non)Bayesian Curve: Unraveling the Effects of Rounding Errors in Belief Updating," Purdue University Economics Working Papers 1353, Purdue University, Department of Economics.
    • Handle: RePEc:pur:prukra:1353
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    References listed on IDEAS

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    Keywords

    Rounding Bias; Measurement Errors; Bayesian Updating; Belief Updating; Learning; Conservatism; Base-Rate Neglect; Econometrics; Hierarchical Bayesian Models;
    All these keywords.

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