arXiv subjects

Christopher Turansick

Publications and source records attributed to Christopher Turansick.

10 recordsLinked to original sources

arXiv2026

In Search of Lost Correlation: Correlated Equilibrium via Marginal Actions

In this paper, we study which data can be induced by a correlated equilibrium given a known finite simultaneous move game. We assume that an analyst has access to the frequency of each agent's actions but does not have access to the distribution over joint action profiles. We characterize which sets of marginal distributions over actions arise from some correlated equilibria via a type of no arbitrage condition. An outside observer is unable to make a profit in expectation by independently contracting with each agent and collecting a portion of the total utility gained via unilateral deviation. This characterization naturally extends to Nash equilibria.

econ.TH↗

arXiv2026

Identification in Stochastic Choice

We characterize the identified sets of a wide range of stochastic choice models, including random utility, various models of boundedly-rational behavior, and dynamic discrete choice. In each of these settings, we show two distributions over choice rules are observationally equivalent if and only if they can be obtained from one another via a finite sequence of simple swapping transforms. We leverage this to obtain complete descriptions of both the defining inequalities and extreme points of these identified sets. In cases where choice frequencies vary smoothly with some parameters, we provide a novel global-inverse result for practically testing identification.

econ.TH↗

arXiv2025

Revealed Social Networks

The linear-in-means model is the standard empirical model of peer effects and asks that an agent's choice or outcome is a combination of their ideal point and the mean outcome of their group. Using choice data and exogenous group variation, we develop a revealed preference style test for the linear-in-means model. This test is formulated as a linear program and can be interpreted as a condition about differentiating the behavior of each agent in a consistent manner. We then study the identification properties of the linear-in-means model. A key takeaway from our analysis is the close relationship between the dimension of the outcome variable and identification. When the outcome variable is one-dimensional, failures of identification are generic. When the outcome variable is multi-dimensional, we provide natural conditions under which identification is generic.

econ.TH↗

arXiv2024

Consumption Dependent Random Utility

We study a dynamic random utility model that allows for consumption dependence. We axiomatically analyze this model and find insights that allow us to distinguish between behavior that arises due to consumption dependence and behavior that arises due to state dependence. As part of our analysis, we show that it is impossible to distinguish between myopic and dynamically sophisticated agents when there are well defined marginal choices in each period. Building on our axiomatic analysis, we develop a revealed preference test for consumption dependent random utility. Our test can be implemented with real data, and we show that our test offers computational improvements over the natural extension of Kitamura and Stoye (2018) to our environment.

econ.TH↗

arXiv2024

Identifying Restrictions on the Random Utility Model

We characterize those ex-ante restrictions on the random utility model which lead to identification. We first identify a simple class of perturbations which transfer mass from a suitable pair of preferences to the pair formed by swapping certain compatible lower contour sets. We show that two distributions over preferences are behaviorally equivalent if and only if they can be obtained from each other by a finite sequence of such transformations. Using this, we obtain specialized characterizations of which restrictions on the support of a random utility model yield identification, as well as of the extreme points of the set of distributions rationalizing a given data set. Finally, when a model depends smoothly on some set of parameters, we show that under mild topological assumptions, identification is characterized by a straightforward, local test.

econ.TH↗

arXiv2024

The Limits of Identification in Discrete Choice

This paper uncovers tight bounds on the number of preferences permissible in identified random utility models. We show that as the number of alternatives in a discrete choice model becomes large, the fraction of preferences admissible in an identified model rapidly tends to zero. We propose a novel sufficient condition ensuring identification, which is strictly weaker than some of those existing in the literature. While this sufficient condition reaches our upper bound, an example demonstrates that this condition is not necessary for identification. Using our new condition, we show that the classic ``Latin Square" example from social choice theory is identified from stochastic choice data.

econ.TH↗

arXiv2023

An Alternative Approach for Nonparametric Analysis of Random Utility Models

We readdress the problem of nonparametric statistical testing of random utility models proposed in Kitamura and Stoye (2018). Although their test is elegant, it is subject to computational constraints which leaves execution of the test infeasible in many applications. We note that much of the computational burden in Kitamura and Stoye's test is due to their test defining a polyhedral cone through its vertices rather than its faces. We propose an alternative but equivalent hypothesis test for random utility models. This test relies on a series of equality and inequality constraints which defines the faces of the corresponding polyhedral cone. Building on our testing procedure, we develop a novel axiomatization of the random utility model.

econ.TH↗

arXiv2023

Intertemporal Aggregation of Choice Data with Consumption Dependent Preferences

We study consumption dependence in the context of random utility and repeated choice. We show that, in the presence of consumption dependence, the random utility model is a misspecified model of repeated rational choice. This misspecification leads to biased estimators and failures of standard random utility axioms. We characterize exactly when and by how much the random utility model is misspecified when utilities are consumption dependent.

econ.TH↗

arXiv2021

Correlated Choice

We study random joint choice rules, allowing for interdependence of choice across agents. These capture random choice by multiple agents, or a single agent across goods or time periods. Our interest is in separable choice rules, where each agent can be thought of as acting independently of the other. A random joint choice rule satisfies marginality if for every individual choice set, we can determine the individual's choice probabilities over alternatives independently of the other individual's choice set. We offer two characterizations of random joint choice rules satisfying marginality in terms of separable choice rules. While marginality is a necessary condition for separability, we show that it fails to be sufficient. We provide an additional condition on the marginal choice rules which, along with marginality, is sufficient for separability.

econ.TH↗

arXiv2021

Identification in the Random Utility Model

The random utility model is known to be unidentified, but there are times when the model admits a unique representation. We offer two characterizations for the existence of a unique random utility representation. Our first characterization puts conditions on a graphical representation of the data set. Non-uniqueness arises when multiple inflows can be assigned to multiple outflows on this graph. Our second characterization provides a direct test for uniqueness given a random utility representation. We also show that the support of a random utility representation is identified if and only if the representation itself is identified.

econ.TH↗