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Econometric Methods For Network Data
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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 2020-05-15 with total page 246 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 Econometric Methods for Network Data by : Michael P. Leung
Download or read book Econometric Methods for Network Data written by Michael P. Leung and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation studies inference in network-formation models with game-theoretic foundations. These are discrete-choice models in which the binary outcome represents whether or not a pair of nodes forms a link. Strategic interactions result from "network externalities, " meaning that the surplus that a node pair enjoys from forming a link may depend on the existence of other links in the network. Estimation of strategic models faces two core difficulties. The first is that network externalities can generate link "autocorrelation, " since an ego's decision to form a link with an alter may depend on the alter's other link-formation decisions and vice versa. Moreover, we typically observe only a few networks in the data and often only a single network. Hence, it is nontrivial to obtain a central limit theorem in the strategic context. The development of a sampling theory for large networks remains an open problem and is a central theme of this dissertation. The second core difficulty is incompleteness due to multiple equilibria. For a fixed vector of node primitives there may be multiple networks consistent with the equilibrium restrictions imposed by the model. If the econometrician is unwilling to take a stance on the mechanism by which nodes coordinate on a particular equilibrium, then the model likelihood depends on an infinite-dimensional nuisance parameter, and the model may only be partially identified. The first chapter of this dissertation analyzes strategic models of network formation with incomplete information. We show that in a setting without unobserved heterogeneity, by conditioning on commonly known attributes, we can eliminate autocorrelation among links. Moreover, we show that equilibrium beliefs can be estimated directly from the data under the restriction that the observed equilibrium is symmetric. Then the structural parameters can be estimated using a simple two-step estimator that augments commonly used "dyadic regression" models with an additional nonparametric first step to account for network externalities. The second chapter studies models with complete information, allowing for unobserved heterogeneity. This chapter considers models that obey a weak "component externalities" restriction on network externalities. We derive conditions under which certain node-level functions of the network constitute alpha-mixing random fields, objects for which central limit theorems exist. In particular, homophily plays an important role in reducing autocorrelation. Our results enable the estimation of certain network moments that are useful for inference. The third chapter studies models with complete information under a stronger "local externalities" restriction on network externalities. Whereas a central limit theorem under component externalities requires a "subcritical" network comprised of a large number of small components, we show that a class of models obeying local externalities can generate sparse networks with giant components, properties consistent with real-world social networks. Further, we develop conditions under which certain network statistics, converge to their expectations as the size of the network goes to infinity. A key requirement is that nodes are homophilous with respect to a set of attributes and that the degree of homophily increases with the size of the network at a particular rate. That is, nodes are increasingly selective about their partners the larger the pool of available partners. The rate at which selectivity increases in part determines the "realism" of global properties of large networks and the possibility of a law of large numbers. We derive rates that are compatible with both objectives. We also develop moment inequalities for inference that are "sharp" in the sense that they fully exhaust the empirical implications of the equilibrium restrictions.
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 Publishing Limited. This book was released on 2020-10-19 with total page 0 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.
Download or read book Network Data written by Bryan S. Graham and published by . This book was released on 2019 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many economic activities are embedded in networks: sets of agents and the (often) rivalrous relationships connecting them to one another. Input sourcing by firms, interbank lending, scientific research, and job search are four examples, among many, of networked economic activities. Motivated by the premise that networks' structures are consequential, this chapter describes econometric methods for analyzing them. I emphasize (i) dyadic regression analysis incorporating unobserved agent-specific heterogeneity and supporting causal inference, (ii) techniques for estimating, and conducting inference on, summary network parameters (e.g., the degree distribution or transitivity index); and (iii) empirical models of strategic network formation admitting interdependencies in preferences. Current research challenges and open questions are also discussed.
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 Econometrics with Machine Learning by : Felix Chan
Download or read book Econometrics with Machine Learning written by Felix Chan and published by Springer Nature. This book was released on 2022-09-07 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in ‘big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice.
Book Synopsis Developing Econometrics by : Hengqing Tong
Download or read book Developing Econometrics written by Hengqing Tong and published by John Wiley & Sons. This book was released on 2011-11-28 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Theories and Methods with Applications to Economics and Business highlights recent advances in statistical theory and methods that benefit econometric practice. It deals with exploratory data analysis, a prerequisite to statistical modelling and part of data mining. It provides recently developed computational tools useful for data mining, analysing the reasons to do data mining and the best techniques to use in a given situation. Provides a detailed description of computer algorithms. Provides recently developed computational tools useful for data mining Highlights recent advances in statistical theory and methods that benefit econometric practice. Features examples with real life data. Accompanying software featuring DASC (Data Analysis and Statistical Computing). Essential reading for practitioners in any area of econometrics; business analysts involved in economics and management; and Graduate students and researchers in economics and statistics.
Book Synopsis Applied Econometrics with R by : Christian Kleiber
Download or read book Applied Econometrics with R written by Christian Kleiber and published by Springer Science & Business Media. This book was released on 2008-12-10 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.
Book Synopsis The Econometrics of Structural Change, Neural Network and Panel Data Analysis by : Chien-Fu Jeff Lin
Download or read book The Econometrics of Structural Change, Neural Network and Panel Data Analysis written by Chien-Fu Jeff Lin and published by . This book was released on 1992 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advances in Panel and Network Econometrics by : Gabriela Miyazato Szini
Download or read book Advances in Panel and Network Econometrics written by Gabriela Miyazato Szini and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "In most of the chapters of this dissertation, I contribute to the literature on the econometrics of network models. While network data has some similarities to traditional panel data, estimating network models poses added challenges demanding new econometric techniques. In particular, part of the network dependence structure is often considered by including unit-specific effects for each pair of units, which imposes new challenges in estimating non-linear models. While most available methods provide consistent estimates and valid inference under settings of dense networks, there are still a lot of open questions regarding the estimation for sparse settings. The second chapter of this dissertation illustrates how to obtain the asymptotic properties of estimators for network (dyadic) models using tools from the literature on U-statistics, and the third chapter proposes a model and an estimation method for the conditional cumulative distribution function of outcomes generated by a sparse network.Finally, the fourth chapter of this dissertation focuses on a different topic in econometrics of panel data models, namely, the estimation of treatment effects and the use of the synthetic control (SC), demeaned synthetic control (DSC), and synthetic difference-in-differences (SDID) methods. More specifically, this chapter re-evaluates the effect of the Brexit referendum on the UK's GDP by considering these different estimation methods. Even though when initially proposed in the literature, the DSC and the SDID do not allow for matching on covariates, we fill this gap by providing an estimation method that takes covariates into account. Moreover, we show that the SDID estimator minimizes both interpolation and extrapolation biases, while the SC method only minimizes the latter."--
Book Synopsis Handbook of Computational Econometrics by : David A. Belsley
Download or read book Handbook of Computational Econometrics written by David A. Belsley and published by John Wiley & Sons. This book was released on 2009-08-18 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Computational Econometrics examines the state of the art of computational econometrics and provides exemplary studies dealing with computational issues arising from a wide spectrum of econometric fields including such topics as bootstrapping, the evaluation of econometric software, and algorithms for control, optimization, and estimation. Each topic is fully introduced before proceeding to a more in-depth examination of the relevant methodologies and valuable illustrations. This book: Provides self-contained treatments of issues in computational econometrics with illustrations and invaluable bibliographies. Brings together contributions from leading researchers. Develops the techniques needed to carry out computational econometrics. Features network studies, non-parametric estimation, optimization techniques, Bayesian estimation and inference, testing methods, time-series analysis, linear and nonlinear methods, VAR analysis, bootstrapping developments, signal extraction, software history and evaluation. This book will appeal to econometricians, financial statisticians, econometric researchers and students of econometrics at both graduate and advanced undergraduate levels.
Book Synopsis Building Better Econometric Models Using Cross Section and Panel Data by : Jeffrey A. Edwards
Download or read book Building Better Econometric Models Using Cross Section and Panel Data written by Jeffrey A. Edwards and published by Business Expert Press. This book was released on 2014-05-01 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employ—a methodology that will yield a model that is both more informative and is a better representation of the data. This book outlines simple, practical procedures that can be used to specify a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book demonstrates how to test for model misspecification and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the researcher’s confidence in the output generated. By following this procedure, researchers will be led to a better, more finely tuned empirical model that yields better results.
Book Synopsis Econometric Modeling and Inference by : Jean-Pierre Florens
Download or read book Econometric Modeling and Inference written by Jean-Pierre Florens and published by Cambridge University Press. This book was released on 2007-07-02 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents the main statistical tools of econometrics, focusing specifically on modern econometric methodology. The authors unify the approach by using a small number of estimation techniques, mainly generalized method of moments (GMM) estimation and kernel smoothing. The choice of GMM is explained by its relevance in structural econometrics and its preeminent position in econometrics overall. Split into four parts, Part I explains general methods. Part II studies statistical models that are best suited for microeconomic data. Part III deals with dynamic models that are designed for macroeconomic and financial applications. In Part IV the authors synthesize a set of problems that are specific to statistical methods in structural econometrics, namely identification and over-identification, simultaneity, and unobservability. Many theoretical examples illustrate the discussion and can be treated as application exercises. Nobel Laureate James A. Heckman offers a foreword to the work.
Book Synopsis Econometric Models of Network Formation by : Áureo Nilo de Paula Neto
Download or read book Econometric Models of Network Formation written by Áureo Nilo de Paula Neto and published by . This book was released on 2020 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: This article provides a selective review on the recent literature on econometric models of network formation. The survey starts with a brief exposition on basic concepts and tools for the statistical description of networks. I then offer a review of dyadic models, focussing on statistical models on pairs of nodes and describe several developments of interest to the econometrics literature. The article also presents a discussion of non-dyadic models where link formation might be influenced by the presence or absence of additional links, which themselves are subject to similar influences. This is related to the statistical literature on conditionally specified models and the econometrics of game theoretical models. I close with a (non-exhaustive) discussion of potential areas for further development.
Download or read book Handbook of Econometrics written by and published by Elsevier. This book was released on 2020-11-25 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Econometrics, Volume 7A, examines recent advances in foundational issues and "hot" topics within econometrics, such as inference for moment inequalities and estimation of high dimensional models. With its world-class editors and contributors, it succeeds in unifying leading studies of economic models, mathematical statistics and economic data. Our flourishing ability to address empirical problems in economics by using economic theory and statistical methods has driven the field of econometrics to unimaginable places. By designing methods of inference from data based on models of human choice behavior and social interactions, econometricians have created new subfields now sufficiently mature to require sophisticated literature summaries. Presents a broader and more comprehensive view of this expanding field than any other handbook Emphasizes the connection between econometrics and economics Highlights current topics for which no good summaries exist
Book Synopsis The Art and Science of Econometrics by : Ping Zong
Download or read book The Art and Science of Econometrics written by Ping Zong and published by Taylor & Francis. This book was released on 2022-05-02 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today econometrics has been widely applied in the empirical study of economics. As an empirical science, econometrics uses rigorous mathematical and statistical methods for economic problems. Understanding the methodologies of both econometrics and statistics is a crucial departure for econometrics. The primary focus of this book is to provide an understanding of statistical properties behind econometric methods. Following the introduction in Chapter 1, Chapter 2 provides the methodological review of both econometrics and statistics in different periods since the 1930s. Chapters 3 and 4 explain the underlying theoretical methodologies for estimated equations in the simple regression and multiple regression models and discuss the debates about p-values in particular. This part of the book offers the reader a richer understanding of the methods of statistics behind the methodology of econometrics. Chapters 5–9 of the book are focused on the discussion of regression models using time series data, traditional causal econometric models, and the latest statistical techniques. By concentrating on dynamic structural linear models like state-space models and the Bayesian approach, the book alludes to the fact that this methodological study is not only a science but also an art. This work serves as a handy reference book for anyone interested in econometrics, particularly in relevance to students and academic and business researchers in all quantitative analysis fields.
Book Synopsis Neural Networks in Finance by : Paul D. McNelis
Download or read book Neural Networks in Finance written by Paul D. McNelis and published by Elsevier. This book was released on 2005-01-20 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. * Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance * Includes numerous examples and applications * Numerical illustrations use MATLAB code and the book is accompanied by a website
