Partial Identification in Matching Models for the Marriage Market

We study partial identification of the preference parameters in models of one-to-one matching with perfectly transferable utilities, without imposing parametric distributional restrictions on the unobserved heterogeneity and with data on one large market. We provide a tractable characterisation of the identified set, under various classes of nonparametric distributional assumptions on the unobserved heterogeneity. Using our methodology, we re-examine some of the relevant questions in the empirical literature on the marriage market which have been previously studied under the Multinomial Logit assumption

An Econometric Model of Network Formation with an Application to Board Interlocks between Firms

The paper provides a framework for partially identifying the parameters governing agents’ preferences in a static game of network formation with interdependent link decisions, complete information, and transferable or non-transferable payoffs. The proposed methodology attenuates the computational difficulties arising at the inference stage - due to the huge number of moment inequalities characterising the sharp identified set and the impossibility of brute-force calculating the integrals entering them - by decomposing the network formation game into local games which have a structure similar to entry games and are such that the network formation game is in equilibrium if and only if each local game is in equilibrium. As an empirical illustration of the developed procedure, the paper estimates firms’ incentives for having executives sitting on the board of competitors, using Italian data

An Econometric Model of Network Formation with an Application to Board Interlocks between Firms

The paper provides a framework for partially identifying the parameters governing agents’ preferences in a static game of network formation with interdependent link decisions, complete information, and transferable or non-transferable payoffs. The proposed methodology attenuates the computational difficulties arising at the inference stage - due to the huge number of moment inequalities characterising the sharp identified set and the impossibility of brute-force calculating the integrals entering them - by decomposing the network formation game into local games which have a structure similar to entry games and are such that the network formation game is in equilibrium if and only if each local game is in equilibrium. As an empirical illustration of the developed procedure, the paper estimates firms’ incentives for having executives sitting on the board of competitors, using Italian data

Identification and inference in discrete choice models with imperfect information

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

Identification and inference in discrete choice models with imperfect information

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

Identification and inference in discrete choice models with imperfect information

We study identification of preferences in a single-agent, static, discrete choice model where the decision maker may be imperfectly informed about the utility generated by the available alternatives. We impose no restrictions on the information frictions the decision maker may face and impose weak assumptions on how the decision maker deals with the uncertainty induced by those frictions. We leverage on the notion of one-player Bayes Correlated Equilibrium in Bergemann and Morris (2013; 2016) to provide a tractable characterisation of the identified set and discuss inference. We use our methodology and data on the 2017 UK general election to estimate a spatial model of voting under weak assumptions on the information that voters have about the returns to voting. We find that the assumptions on the information environment can drive the interpretation of voter preferences. Counterfactual exercises quantify the consequences of imperfect information in politics

Partial identification in matching models for the marriage market

We consider the one-to-one matching models with transfers of Choo and Siow (2006) and Galichon and Salanié (2015). When the analyst has data on one large market only, we study identification of the systematic components of the agents’ preferences without imposing parametric restrictions on the probability distribution of the latent variables. Specifically, we provide a tractable characterisation of the region of parameter values that exhausts all the implications of the model and data (the sharp identified set), under various classes of nonparametric distributional assumptions on the unobserved terms. We discuss a way to conduct inference on the sharp identified set and conclude with Monte Carlo simulations

Price Competition and Endogenous Product Choice in Networks: Evidence from the US Airline Industry

We develop a two-stage game in which competing airlines first choose the networks of markets to serve in the first stage before competing in price in the second stage. Spillovers in entry decisions across markets are allowed, which accrue on the demand, marginal cost, and fixed cost sides. We show that the second-stage parameters are point identified, and we design a tractable procedure to set identify the first-stage parameters and to conduct inference. Further, we estimate the model using data from the domestic US airline market and find significant spillovers in entry. In a counterfactual exercise, we evaluate the 2013 merger between American Airlines and US Airways. Our results highlight that spillovers in entry and post-merger network readjustments play an important role in shaping post-merger outcomes

An Econometric Model of Network Formation with an Application to Board Interlocks between Firms

We study identification of the players’ preferences in a network formation game featuring complete information, nonreciprocal links, and a spillover effect. We decompose the network formation game into local games such that the network formation game is in equilibrium if and only if each local game is in equilibrium. This decomposition helps us prove equilibrium existence, reduce the number of moment inequalities characterising the identified set, and simplify the calculation of the integrals entering those moment inequalities. The developed methodology is used to investigate Italian firms’ incentives for having their executive directors sitting on competitors’ boards

Partial identification in matching models for the marriage market

We consider the one-to-one matching models with transfers of Choo and Siow (2006) and Galichon and Salanié (2015). When the analyst has data on one large market only, we study identification of the systematic components of the agents’ preferences without imposing parametric restrictions on the probability distribution of the latent variables. Specifically, we provide a tractable characterisation of the region of parameter values that exhausts all the implications of the model and data (the sharp identified set), under various classes of nonparametric distributional assumptions on the unobserved terms. We discuss a way to conduct inference on the sharp identified set and conclude with Monte Carlo simulations