An econometric model of network formation with an application to board interlocks between firms

Gualdani, Cristina

doi:10.1016/j.jeconom.2019.08.015

Cited by 13 publications

(5 citation statements)

References 77 publications

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“…As with the example of pure strategy Nash equilibrium, this can be used to derive a partial identification result for the utility functions (for more on these solution concepts, see, e.g., Jackson 2010, chapter 6). Related partial identification results have been derived by Miyauchi (2016), de Paula et al (2018, Sheng (2020), andGualdani (2021).…”

Section: Multiple Decision Makersmentioning

confidence: 99%

Recent Developments in Partial Identification

Kline,

Tamer

2023

Annual Review of Economics

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Identification strategies concern what can be learned about the value of a parameter based on the data and the model assumptions. The literature on partial identification is motivated by the fact that it is not possible to learn the exact value of the parameter for many empirically relevant cases. A typical result in the literature on partial identification is a statement about characterizing the identified set, which summarizes what can be learned about the parameter of interest given the data and model assumptions. For instance, this may mean that the value of the parameter can be learned to be necessarily within some set of values. First, the review surveys the general frameworks that have been developed for conducting a partial identification analysis. Second, the review surveys some of the more recent results on partial identification.

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Section: Multiple Decision Makersmentioning

confidence: 99%

Recent Developments in Partial Identification

Kline,

Tamer

2023

Annual Review of Economics

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“…To avoid this difficulty, a large part of the literature has been trying to provide a confidence region for an outer set , that is, a collection of values for the parameter of interest that contains the identified set but may also contain additional values 1 . Because of its tractability, constructing a confidence region for an outer set has been entertained in various topics of studies where the parameters of interest are only partially identified; see, for instance, Blundell, Gosling, Ichimura, and Meghir (2007), Ciliberto and Tamer (2009), Aucejo, Bugni, and Hotz (2017), Sheng (2020), De Paula, Richards‐Shubik, and Tamer (2018), Dickstein and Morales (2018), Honoré and Hu (2020), Chesher and Rosen (2020), Gualdani (2021), and Berry and Compiani (2023), among many others.…”

Section: Introductionmentioning

confidence: 99%

“…33 See, for instance, Sheng (2020), Gualdani (2021), and the empirical application in Berry and Compiani (2023, Section 6, footnote 42). …”

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confidence: 99%

Discordant relaxations of misspecified models

Li,

Kédagni,

Mourifié

2024

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In many set‐identified models, it is difficult to obtain a tractable characterization of the identified set. Therefore, researchers often rely on nonsharp identification conditions, and empirical results are often based on an outer set of the identified set. This practice is often viewed as conservative yet valid because an outer set is always a superset of the identified set. However, this paper shows that when the model is refuted by the data, two sets of nonsharp identification conditions derived from the same model could lead to disjoint outer sets and conflicting empirical results. We provide a sufficient condition for the existence of such discordancy, which covers models characterized by conditional moment inequalities and the Artstein (1983) inequalities. We also derive sufficient conditions for the nonexistence of discordant submodels, therefore providing a class of models for which constructing outer sets cannot lead to misleading interpretations. In the case of discordancy, we follow Masten and Poirier (2021) by developing a method to salvage misspecified models, but unlike them, we focus on discrete relaxations. We consider all minimum relaxations of a refuted model that restores data consistency. We find that the union of the identified sets of these minimum relaxations is robust to detectable misspecifications and has an intuitive empirical interpretation.

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“…Specifically, the model in equation ( 1) can be derived as a stable outcome in a static game. Papers that study the strategic formation of a network as a static game include Goldsmith-Pinkham and Imbens (2013); Leung (2015a,b); Menzel (2015); Miyauchi (2016); Boucher and Mourifié (2017); de Paula, Richards-Shubik, and Tamer (2017); Mele (2017); Candelaria and Ura (2018); Sheng (2018); Gualdani (2020), and Ridder and Sheng (2020). The authors study network formation models that account for network externalities.…”

Section: Introductionmentioning

confidence: 99%

A Semiparametric Network Formation Model with Unobserved Linear Heterogeneity

Candelaria

2020

Preprint

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This paper analyzes a semiparametric model of network formation in the presence of unobserved agent-specific heterogeneity. The objective is to identify and estimate the preference parameters associated with homophily on observed attributes when the distributions of the unobserved factors are not parametrically specified. This paper offers two main contributions to the literature on network formation. First, it establishes a new point identification result for the vector of parameters that relies on the existence of a special regressor. The identification proof is constructive and characterizes a closed-form for the parameter of interest. Second, it introduces a simple two-step semiparametric estimator for the vector of parameters with a first-step kernel estimator. The estimator is computationally tractable and can be applied to both dense and sparse networks. Moreover, I show that the estimator is consistent and has a limiting normal distribution as the number of individuals in the network increases. Monte Carlo experiments demonstrate that the estimator performs well in finite samples and in networks with different levels of sparsity.

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An econometric model of network formation with an application to board interlocks between firms

Cited by 13 publications

References 77 publications

Recent Developments in Partial Identification

Recent Developments in Partial Identification

Discordant relaxations of misspecified models

A Semiparametric Network Formation Model with Unobserved Linear Heterogeneity

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