Ruolei Zhu
I am a PhD candidate in Economics at University College London. My research interests are in structural econometrics, discrete choice models, and computational methods, with a current focus on rational inattention models.
I will be on the 2026–27 job market.
Research
01 JOB MARKET PAPER
Estimation of Rational Inattention–Additive Random Utility Models
Abstract & paper
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.
02 WORKING PAPER
Demand Estimation for Rationally Inattentive Consumers
Abstract & paper
We develop a rational-inattention BLP (RI–BLP) model for demand estimation from aggregate market shares, allowing for endogenous prices and unobserved preference heterogeneity. We establish conditions for uniqueness of market-share inversion and construct a simulated RI–BLP GMM estimator with instrumental-variable moments and market-share constraints. Under regularity and simulation-accuracy conditions, the estimator is consistent and asymptotically normal as the number of markets grows, with the number of products fixed. Monte Carlo experiments show accurate recovery of mean utility coefficients, with less precise estimates of dispersion parameters. In a canned-tuna application, RI–BLP and logit yield similar average own-price elasticities. Unlike logit, the fitted RI–BLP model implies that a price increase can reduce demand even for similar products and shift demand toward the outside option.
03 WORKING PAPER
Identification of Rational Inattention–Additive Random Utility Models
(with Dennis Kristensen)
Abstract
This paper introduces a novel class of econometric models, called Rational Inattention–Additive Random Utility Models (RI–ARUMs). It is based on the discrete choice rational inattention model of Matějka and McKay (2015), with additive unobserved heterogeneity incorporated. To our knowledge, this class of models includes all known parametric models that have been used in empirical work up to now. Under our identification assumptions, we show that the deterministic payoff functions are nonparametrically identified even if the distributions of unobserved variables, decision makers’ priors, and the information cost are all unknown to the econometrician. The identification strategy relies on fundamental results from convex optimisation theory.
Paper coming soon
Teaching
University College London
Teaching Assistant
Statistical Learning for Public Policy (MSc Data Science and Public Policy)
Mathematics for Economics (BSc Economics, Year 1)
Machine Learning in Economics (MSc Economics)
Microeconomics (BSc Economics, Year 2)
References