A survey of preference estimation with unobserved choice set heterogeneity

Crawford, Gregory S.; Griffith, Rachel; Iaria, Alessandro

doi:10.1016/j.jeconom.2020.07.024

Cited by 35 publications

(24 citation statements)

References 70 publications

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“…The second group of papers rely on exclusion restrictions (e.g., Goeree, 2008;Gaynor, Propper, and Seiler, 2016). The third group of papers relates to the consideration set formation process (e.g., Abaluck and Adams, 2018;Barseghyan, Molinari, and Thirkettle, 2019;Barseghyan, Coughlin, Molinari, and Teitelbaum, 2019;Cattaneo, Ma, Masatlioglu, and Suleymanov, 2019;Crawford, Griffith, and Iaria, 2019). The papers in the third group are close in spirit to our framework.…”

Section: Introductionmentioning

confidence: 79%

Identification in discrete choice models with imperfect information

Gualdani¹,

Sinha²

2019

Preprint

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In this paper we study identification and inference of preference parameters in a single-agent, static, discrete choice model where the decision maker may face attentional limits precluding her to exhaustively process information about the payoffs of the available alternatives. By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris (2016), we provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the rule with which the decision maker resolves ties. Simulations reveal that the obtained bounds on the preference parameters can be tight in several settings of empirical interest.

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Section: Introductionmentioning

confidence: 79%

Identification in discrete choice models with imperfect information

Gualdani¹,

Sinha²

2019

Preprint

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“…The last approach is to rely primarily on restrictions to the choice set formation process. Five recent papers that exemplify this approach are Abaluck and Adams‐Prassl (2021), Barseghyan, Molinari, and Thirkettle (2021), Crawford, Griffith, and Iaria (2020), Lu (2021), and Cattaneo et al (2020). 48…”

Section: Discussionmentioning

confidence: 99%

“…Our core model imposes—and hence our main identification result requires—only one mild assumption on the choice set formation process, namely that choice sets have a known minimum size greater than 1. Importantly, our core model does not assume that choice sets are independent of preferences conditional on observables (Abaluck and Adams‐Prassl (2021), Crawford, Griffith, and Iaria (2020), Cattaneo et al (2020)). Nor do we impose other restrictions on how agents' choice sets are formed (Abaluck and Adams‐Prassl (2021), Barseghyan, Molinari, and Thirkettle (2021)) or evolve over time (Crawford, Griffith, and Iaria (2020)), rely on exclusion restrictions or large support assumptions (Abaluck and Adams‐Prassl (2021), Barseghyan, Molinari, and Thirkettle (2021)), require that the econometrician knows the composition of the smallest possible choice set for each agent (Abaluck and Adams‐Prassl (2021), Lu (2021)), or assume that choice sets satisfy a monotonicity or other regularity condition (Lu (2021), Cattaneo et al (2020)).…”

Section: Discussionmentioning

confidence: 99%

Heterogeneous Choice Sets and Preferences

Barseghyan

Coughlin

Molinari

et al. 2021

ECTA

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We propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between choice sets and preferences. We first characterize the sharp identification region of the model's parameters by a finite set of conditional moment inequalities. We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance. We find that the data can be explained by expected utility theory with low levels of risk aversion and heterogeneous non‐singleton choice sets, and that more than three in four households require limited choice sets to explain their deductible choices. We also provide simulation evidence on the computational tractability of our method in applications with larger feasible sets or higher‐dimensional unobserved heterogeneity.

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“…41 For canonical cites see, e.g., Roberts & Lattin (1991) and Ben-Akiva & Boccara (1995). 42 Time variation is used also in Crawford, Griffith, & Iaria (2020), who show that with panel data and preferences in the logit family, point identification of preferences is possible, without any exclusion restrictions, under the assumption that choice sets and preferences are independent conditional on observables and with restrictions on how choice sets evolve over time. These restrictions enable the construction of proper subsets of DMs' true choice sets ('sufficient sets') that can be utilized to estimate the preference model.…”

Section: Discussionmentioning

confidence: 99%

Discrete choice under risk with limited consideration

Barseghyan

Molinari

Thirkettle

2020

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This paper is concerned with learning decision makers' preferences using data on observed choices from a finite set of risky alternatives. We propose a discrete choice model with unobserved heterogeneity in consideration sets and in standard risk aversion. We obtain sufficient conditions for the model's semi-nonparametric point identification, including in cases where consideration depends on preferences and on some of the exogenous variables. Our method yields an estimator that is easy to compute and is applicable in markets with large choice sets. We illustrate its properties using a dataset on property insurance purchases.

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A survey of preference estimation with unobserved choice set heterogeneity

Cited by 35 publications

References 70 publications

Identification in discrete choice models with imperfect information

Identification in discrete choice models with imperfect information

Heterogeneous Choice Sets and Preferences

Discrete choice under risk with limited consideration

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