arXiv 12 Oct 2026 · Econometrics
arXiv:2610.16701 · PDF · Extracted main text
This paper develops likelihood-based estimation for parametric rational inattention--additive random utility models. We establish consistency and asymptotic normality of the exact maximum likelihood estimator and provide conditions under which the simulated estimator shares its asymptotic distribution. To implement estimation, we develop Newton--BA, a KKT-guided hybrid of Newton--Raphson and Blahut--Arimoto (BA). The algorithm accommodates both interior and boundary solutions and substantially improves computational efficiency relative to direct BA while preserving numerical accuracy. Monte Carlo simulations show that parameter recovery improves with sample size and that confidence-interval coverage is generally close to the nominal $95$ percent level. Finally, we compare our model with no- and perfect-information logit benchmarks in an empirically calibrated ModeCanada experiment. Our model achieves similar in-sample likelihood fit to perfect-information logit but more accurately recovers key preference parameters, choice probabilities, and responses to travel-time changes.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kristensen, Dennis and Zhu, Ruolei (2026) Identification of Rational Inattention-Additive Random Utility Models self | 0.920 | 9 | 5 | 78% |
| 2 | Newey, Whitney K. and McFadden, Daniel (1994) Large Sample Estimation and Hypothesis Testing | 0.836 | 16 | 2 | 88% |
| 3 | Mat ejka, Filip and McKay, Alisdair (2015) Rational inattention to discrete choices: A new foundation for the multinomial logit model | 0.811 | 4 | 2 | 100% |
| 4 | Caplin, Andrew and Dean, Mark and Leahy, John (2019) Rational inattention, optimal consideration sets, and stochastic choice | 0.511 | 2 | 2 | 50% |
| 5 | Bhat, Chandra R (1995) A Heteroscedastic Extreme Value Model of Intercity Travel Mode Choice | 0.511 | 2 | 1 | 100% |
| 6 | Lee, Lung-Fei (1995) Asymptotic Bias in Simulated Maximum Likelihood Estimation of Discrete Choice Models | 0.511 | 2 | 1 | 100% |
| 7 | Armstrong, Timothy B. and Weidner, Martin and Zeleneev, Andrei (2026) Robust Estimation and Inference in Panels with Interactive Fixed Effects | 0.405 | 1 | 1 | 100% |
| 8 | Croissant, Yves (2020) Estimation of Random Utility Models in R: The mlogit Package | 0.405 | 1 | 1 | 100% |
| 9 | Gouriéroux, Christian and Monfort, Alain (1996) Simulation-Based Econometric Methods | 0.405 | 1 | 1 | 100% |
| 10 | Lee, Lung-Fei (1992) On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 25 scored citations.