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Identification And Estimation Of Network Formation Models
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Book Synopsis Identification and Estimation of Network Formation Models by : Jun Sung Kim
Download or read book Identification and Estimation of Network Formation Models written by Jun Sung Kim and published by . This book was released on 2014 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: My thesis studies identification and estimation in network formation models. First, I study what can be learned from pairwise stable networks. Pairwise stability of a network gives strong identification power when I consider the probability that the observed network is pairwise stable. I propose a semiparametric maximum score estimator which is simple and computationally feasible. I apply the empirical model to social and economic networks in rural India, and find homophily patterns in village networks. Second, I propose a structural model of multigraph formation, where 1) individuals determine multiple types of links simultaneously; 2) all networks interact with each other; and 3) one or more networks are endogenous but not simultaneous. I extend the notion of pairwise stability to a multigraph, and show that the structural model is equivalent to a multinomial choice model. The presence of endogenous but not simultaneous networks is a source of an incomplete econometric model. Relying on partially identified econometric models, I characterize the sharp identification region of parameters by a finite set of moment inequalities. I apply the model to village networks and find that friendship affects risk sharing and favor exchange networks in the same direction. The last chapter studies an empirical model of network formation in the U.S. airline industry and investigates the size of network externalities. I assume that each airline builds a network that satisfies a weak notion of stability. That is, no airlines want to deviate from their current networks by a single route change. In this framework, I can use an entry game to investigate the airline industry and include network measures in the profit function to estimate network externalities. I find that when I control for the number of one-stop flights the effect of hub-size is larger than the case without considering one-stop flights.
Book Synopsis Nonparametric Identification and Estimation of Distance Functions in Network Formation Models with Fixed Effects by : Peter Toth
Download or read book Nonparametric Identification and Estimation of Distance Functions in Network Formation Models with Fixed Effects written by Peter Toth and published by . This book was released on 2018 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second chapter of the dissertation discusses the non-parametric extension of the network formation model in Toth (2018), when the researcher does not assume the functional form of the distance function. An intuitive way for the non-parametric extension is to use the parametric estimator for linear indices coupled with a series expansion. While the technique is generally applicable, it comes with the caveat that the identification of the models must be assured a priori. After demonstrating the applicability of the method on classical models of Manski (1987) and Han (1987), we prove the nonparametric identification of the distance function for the network formation model, and define the corresponding series estimator. We give a proof for consistency, and also analyze the rate of convergence.
Book Synopsis The Econometric Analysis of Network Data by : Bryan Graham
Download or read book The Econometric Analysis of Network Data written by Bryan Graham and published by Academic Press. This book was released on 2020-06-03 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Econometric Analysis of Network Data serves as an entry point for advanced students, researchers, and data scientists seeking to perform effective analyses of networks, especially inference problems. It introduces the key results and ideas in an accessible, yet rigorous way. While a multi-contributor reference, the work is tightly focused and disciplined, providing latitude for varied specialties in one authorial voice. Answers both 'why' and 'how' questions in network analysis, bridging the gap between practice and theory allowing for the easier entry of novices into complex technical literature and computation Fully describes multiple worked examples from the literature and beyond, allowing empirical researchers and data scientists to quickly access the 'state of the art' versioned for their domain environment, saving them time and money Disciplined structure provides latitude for multiple sources of expertise while retaining an integrated and pedagogically focused authorial voice, ensuring smooth transition and easy progression for readers Fully supported by companion site code repository 40+ diagrams of 'networks in the wild' help visually summarize key points
Book Synopsis Two-Step Estimation of Network-Formation Models with Incomplete Information by : Michael P. Leung
Download or read book Two-Step Estimation of Network-Formation Models with Incomplete Information written by Michael P. Leung and published by . This book was released on 2015 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: We model network formation as a simultaneous game of incomplete information, allowing linking decisions to depend on the structure of the network as well as the attributes of agents. When the data is rationalized by a symmetric equilibrium, meaning observationally equivalent agents choose the same ex-ante strategies, the model can be estimated using a computationally simple two-step estimator. We derive its asymptotic properties under a sequence of models sending the number of agents to infinity, which enables inference with only a single network observation. Our procedure generalizes dyadic regression, allowing the latent index to be a function of endogenous regressors that depend on the network. We apply the estimator to study trust networks in rural Indian villages.
Book Synopsis Semiparametric Estimation in Network Formation Models with Homophily and Degree Heterogeneity by : Peter Toth
Download or read book Semiparametric Estimation in Network Formation Models with Homophily and Degree Heterogeneity written by Peter Toth and published by . This book was released on 2017 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers a semiparametric version of the network formation model of Graham (2017). The two-way fixed-effects binary choice model allows for homophily and degree heterogeneity, but unlike Graham (2017) leaves the distribution of pair-specific unobservables unspecified. Identification of the slope parameters and fixed effects follows from a novel approach that does not rely on distributional assumptions. The identification strategy suggests an estimator for the slope parameters based upon tetrads of nodes within the network. A computationally simple version of this estimator is shown to be consistent with a non-parametric convergence rate. A consistent estimator of the fixed effects is also provided. Partial identification, for the case of discrete covariate support, and an extension to nonlinear fixed effects are also considered.
Book Synopsis Estimation of a Scale-Free Network Formation Model by : Anton Kolotilin
Download or read book Estimation of a Scale-Free Network Formation Model written by Anton Kolotilin and published by . This book was released on 2014 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Essays on the Identification and Estimation of Network Models by : Yiran Xie
Download or read book Essays on the Identification and Estimation of Network Models written by Yiran Xie and published by . This book was released on 2022 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of three main chapters that study social interactions in networks. In Chapter 1, I study a market with many-to-many contracts when the number of market participants increases. Many-to-many contracts allow a seller to trade with multiple buyers and a buyer to trade with multiple sellers. I focus on investigating the identification of payoff parameters through data observed from equilibrium matches in a large many-to-many matching market. In many-to-many matching markets, several issues have to be addressed: bias would arise since the outcomes are only observed when links are formed between two agents, and the maximum number of relationships an agent can enter into would possibly affect the set of stable outcomes. I show that under certain conditions, the number of firms (workers) that are willing to be matched to a specific worker (firm) grows at a rate regardless of the capacity of both sides. Furthermore, I show a correspondence between the stable matching outcomes in a many-to-many matching framework and that in a one-to-one matching framework. In Chapter 2, I conduct a structural econometric analysis of the diffusion process with players who observe their neighbors and make decisions based on their neighbors' decisions. I study the identification and estimation of diffusion processes in social and economic networks. Compared to the classic econometric diffusion literature that assumes a continuous population with a stochastic network structure, I provide a new econometric framework to analyze diffusion processes in fixed networks where Bayesian players observe their close neighbors. I demonstrate the existence of the equilibrium of the model and characterize the unique symmetric equilibrium. Based on these theoretical findings, I propose a consistent and tractable two-step estimator for payoff parameters using feasible data from a single large network. I evaluate the finite sample performance using Monte Carlo simulations. Chapter 3 applies the network diffusion model to data on the participation of a microfinance program in Indian villages to describe the impact of neighbors on individual decisions. Our model allows us to study the various network effect across different types of agents who care about their neighbors' opinions. It depends on unknown equilibrium beliefs, which specify agents' expectations about their neighbors' decisions. Using participation data from 43 villages, each including about 200 villagers, I estimate these equilibrium beliefs and the network effects.
Book Synopsis Specification and Estimation of Network Formation and Network Interaction Models with the Exponential Probability Distribution by :
Download or read book Specification and Estimation of Network Formation and Network Interaction Models with the Exponential Probability Distribution written by and published by . This book was released on 2019 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we model network formation and network interactions under a unified framework. The key feature of our model is to allow individuals to respond to incentives that stem from interaction benefits of certain activities when they choose friends (network links), while capturing homophily in terms of unobserved characteristic variables in network formation and activities. There are two advantages of this modeling approach: first, one can evaluate whether incentives from certain interactions are important factors for friendship formation or not. Second, in addition to homophily effects in terms of unobserved characteristics, inclusion of incentive effects in the network formulation also corrects possible friendship selection bias on activity outcomes under network interactions. A theoretical foundation of this unified model is based on a sub-game perfect equilibrium of a two-stage game. A tractable Bayesian MCMC approach is proposed for the estimation of the model, and we demonstrate its finite sample performance in a simulation study. We apply the model to study empirically American high school students' friendship networks from the Add Health dataset. We consider two activity variables, GPA and smoking frequency, and find a significant incentive effect from GPA, but not from smoking, on friendship formation. These results suggest that the benefit of interactions in academic learning is an important factor for friendship formation, whereas the interaction benefit of smoking is not. On the other hand, from the perspective of network interactions, both GPA and smoking frequency are subject to significant positive interaction (peer) effects.
Book Synopsis The Econometrics of Networks by : Áureo de Paula
Download or read book The Econometrics of Networks written by Áureo de Paula and published by Emerald Group Publishing. This book was released on 2020-10-19 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: Showcasing fresh methodological and empirical research on the econometrics of networks, and comprising both theoretical, empirical and policy papers, the authors in this volume bring together a wide range of perspectives to facilitate a dialogue between academics and practitioners for better understanding this groundbreaking field.
Book Synopsis Social Dynamics by : Steven N. Durlauf
Download or read book Social Dynamics written by Steven N. Durlauf and published by MIT Press. This book was released on 2001 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of essays presents a variety of approaches to understanding the dynamics of human interaction.
Book Synopsis An Empirical Network Formation Model with Incomplete Information by : Wenyu Zhou
Download or read book An Empirical Network Formation Model with Incomplete Information written by Wenyu Zhou and published by . This book was released on 2019 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis studies a network formation model with incomplete information, which introduces the neighborhood effect into the analysis of network formation. We show that the model is identified under some mild conditions. To overcome the computational burden, we propose to use the nested pseudo-likelihood algorithm to estimate the parameters of interest. Finite sample performance of the NPL estimation method is investigated through several Monte Carlo experiments. We find that a positive neighborhood effect makes agents more likely to form links, which can increase the network density. Besides, we also discuss three potential research directions.
Book Synopsis The Econometrics of Networks by : Áureo de Paula
Download or read book The Econometrics of Networks written by Áureo de Paula and published by Emerald Group Publishing. This book was released on 2020-10-19 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: Showcasing fresh methodological and empirical research on the econometrics of networks, and comprising both theoretical, empirical and policy papers, the authors in this volume bring together a wide range of perspectives to facilitate a dialogue between academics and practitioners for better understanding this groundbreaking field.
Book Synopsis Network Formation as a Choice Process by : Jan Surya Overgoor
Download or read book Network Formation as a Choice Process written by Jan Surya Overgoor and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding why networks form and evolve the way they do is a core goal of many scientific disciplines ranging from the social to the physical sciences. Across these disciplines, many kinds of formation models have been employed, several of which can be subsumed under a choice framework, using conditional logit models from discrete choice and random utility theory. Each new edge is viewed as a ``choice'' made by a node to connect to another node, based on (generic) features of the other nodes available to make a connection. This perspective on network formation unifies existing models such as preferential attachment, triadic closure, and node fitness, which are all special cases, and thereby provides a flexible means for conceptualizing, estimating, and comparing models. The lens of discrete choice theory also provides several new tools for analyzing sUnderstanding why networks form and evolve the way they do is a core goal of many scientific disciplines ranging from the social to the physical sciences. Across these disciplines, many kinds of formation models have been employed, several of which can be subsumed under a choice framework, using conditional logit models from discrete choice and random utility theory. Each new edge is viewed as a ``choice'' made by a node to connect to another node, based on (generic) features of the other nodes available to make a connection. This perspective on network formation unifies existing models such as preferential attachment, triadic closure, and node fitness, which are all special cases, and thereby provides a flexible means for conceptualizing, estimating, and comparing models. The lens of discrete choice theory also provides several new tools for analyzing social network formation. In large network data logit models run into practical and conceptual issues, since large numbers of alternatives make direct inference intractable and the assumptions underlying the logit model cease to be realistic in large graphs. Importance sampling of non-chosen alternatives reduces the data size significantly, while, under the right conditions, preserving consistency of the estimates. A model simplification technique called ``de-mixing'', whereby mixture models are reformulated to operate over disjoint choice sets, reduces mixed logit models to conditional logit models. This opens the door to the other approaches to scalability and provides a new analytical toolkit to understand the underlying processes. The flexibility of the logit framework is illustrated with examples that analyze several synthetic and real-world datasets, including data from Flickr, Venmo and a large citation graph. The logit model provides a rigorous method for estimating preferential attachment models and can separate the effects of preferential attachment and triadic closure. A more substantial application is the identification of the persistent and changing parts of the networking strategies of U.S. college students as they go through their college years. This analysis is done using a rich and large data set of digital social network data from the Facebook platform.ocial network formation. In large network data logit models run into practical and conceptual issues, since large numbers of alternatives makes direct inference intractable and the assumptions underlying the logit model cease to be realistic in large graphs. Importance sampling of non-chosen alternatives reduces the data size significantly, while, under the right conditions, preserving consistency of the estimates. A model simplification technique called ``de-mixing'', whereby mixture models are reformulated to operate over disjoint choice sets, reduces mixed logit models to conditional logit models. This opens the door to the other approaches to scalability and provides a new analytical toolkit to understand the underlying processes. The flexibility of the logit framework is illustrated with examples that analyze several synthetic and real-world datasets, including data from Flickr, Venmo and a large citation graph. The logit model provides a rigorous method for estimating preferential attachment models and can separate the effects of preferential attachment and triadic closure. A more substantial application is the identification of the persistent and changing parts of the networking strategies of U.S. college students as they go through their college years. This analysis is done using a rich and large data set of digital social network data from the Facebook platform.
Book Synopsis Econometrics of Network Models by : Áureo Nilo de Paula Neto
Download or read book Econometrics of Network Models written by Áureo Nilo de Paula Neto and published by . This book was released on 2015 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this article I provide a (selective) review of the recent econometric literature on networks. I start with a discussion of developments in the econometrics of group interactions. I subsequently provide a description of statistical and econometric models for network formation and approaches for the joint determination of networks and interactions mediated through those networks. Finally, I give a very brief discussion of measurement issues in both outcomes and networks. My focus is on identification and computational issues, but estimation aspects are also discussed.
Book Synopsis Specification and Estimation of Network Formation and Network Interaction Models with the Exponential Probability Distribution by :
Download or read book Specification and Estimation of Network Formation and Network Interaction Models with the Exponential Probability Distribution written by and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we model network formation and network interactions under a unified framework. The key feature of our model is to allow individuals to respond to incentives that stem from interaction benefits of certain activities when they choose friends (network links), while capturing homophily in terms of unobserved characteristic variables in network formation and activities. There are two advantages of this modeling approach: first, one can evaluate whether incentives from certain interactions are important factors for friendship formation or not. Second, in addition to homophily effects in terms of unobserved characteristics, inclusion of incentive effects in the network formulation also corrects possible friendship selection bias on activity outcomes under network interactions. A theoretical foundation of this unified model is based on a sub-game perfect equilibrium of a two-stage game. A tractable Bayesian MCMC approach is proposed for the estimation of the model, and we demonstrate its finite sample performance in a simulation study. We apply the model to study empirically American high school students' friendship networks from the Add Health dataset. We consider two activity variables, GPA and smoking frequency, and find a significant incentive effect from GPA, but not from smoking, on friendship formation. These results suggest that the benefit of interactions in academic learning is an important factor for friendship formation, whereas the interaction benefit of smoking is not. On the other hand, from the perspective of network interactions, both GPA and smoking frequency are subject to significant positive interaction (peer) effects.
Book Synopsis Classification of Network Formation Models by : Jochen Möbert
Download or read book Classification of Network Formation Models written by Jochen Möbert and published by . This book was released on 2006 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Essays on Identification and Estimation of Structural Economic Models by : Shaomin Wu
Download or read book Essays on Identification and Estimation of Structural Economic Models written by Shaomin Wu and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of three chapters that study the identification and estimation of structural economic models. Chapter 1, "Identification and Estimation of Nonseparable Triangular Equations with Mismeasured Instruments" studies the nonparametric identification and estimation of the marginal effect of an endogenous variable X on the outcome variable Y , given a potentially mismeasured instrument variable W∗, without assuming linearity or separability of the functions governing the relationship between observables and unobservables. In order to address the challenges arising from the co-existence of measurement error and nonseparability, I first employ the deconvolution technique from the measurement error literature to identify the joint distribution of Y,X,W∗ using two error-laden measurements of W∗. I then recover the structural derivative of the function of interest and the "Local Average Response" (LAR) from the joint distribution via the "unobserved instrument" approach in Matzkin (2016). I also propose nonparametric estimators for these parameters and derive their uniform rates of convergence. Monte Carlo exercises show evidence that the estimators I propose have goodfinite sample performance. Chapter 2, "Two-step Estimation of Network Formation Models with Unobserved Heterogeneities and Strategic Interactions", characterizes the network formation process as a static game of incomplete information, where the latent payoff of forming a link between two individuals depends on the structure of the network, as well as private information on agents' attributes. I allow agents' private unobserved attributes to be correlated with observed attributes through individual fixed effects. Using data from a single large network, I propose a two-step estimator for the model primitives. In the first step, I estimate agents' equilibrium beliefs of other people's choice probabilities. In the second step, I plug in the first-step estimator to the conditional choice probability expression and estimate the model parameters and the unobserved individual fixed effects together using Joint MLE. Assuming that the observed attributes are discrete, I showed that the first step estimator is uniformly consistent with rate N−1/4, where N is the total number of linking proposals. I also show that the second-step estimator converges asymptotically to a normal distribution at the same rate. Chapter 3, "Identification and Estimation in Differentiated Products Markets Where Firms Affect Consumers' Attention" studies the nonparametric identification and estimation of a demand and supply system where firms affect consumers' consideration sets via costly marketing inputs, when market-level data is available. On the demand side, I characterize preferences and considerations nonparametrically, allowing rich heterogeneities and correlations between them. On the supply side, I characterize firms' optimal choices by a set of first-order conditions without specifying the form of the oligopoly model. The demand and supply sides form a simultaneous system of equations in the spirit of Berry and Haile (2014). I then show the identification of the system using the method proposed by Matzkin (2015). Moreover, using the variations of exclusive regressors entering preferences and considerations respectively, I separately identify features of the utility functions and the attention functions. Based on the constructive identification results, I propose nonparametric estimators of the demand, utility, and attention functions and show their asymptotic properties.
