The Econometric Analysis of Network Data

• Author : Bryan Graham,Aureo de Paula
• Publisher : Academic Press
• Release Date : 2020-06-01
• ISBN : 9780128117729

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

• Author : Áureo de Paula,Elie Tamer,Marcel-Cristian Voia
• Publisher : Emerald Group Publishing
• Release Date : 2020-10-19
• ISBN : 9781838675776

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.

• Author : Yann Bramoullé,Andrea Galeotti,Brian Rogers
• Publisher : Oxford University Press
• Release Date : 2016-03-01
• ISBN : 9780199948284

The Oxford Handbook of the Economics of Networks represents the frontier of research into how and why networks they form, how they influence behavior, how they help govern outcomes in an interactive world, and how they shape collective decision making, opinion formation, and diffusion dynamics. From a methodological perspective, the contributors to this volume devote attention to theory, field experiments, laboratory experiments, and econometrics. Theoretical work in network formation, games played on networks, repeated games, and the interaction between linking

• Author : Katarzyna Kopczewska
• Publisher : Routledge
• Release Date : 2020-11-26
• ISBN : 9781000079784

This textbook is a comprehensive introduction to applied spatial data analysis using R. Each chapter walks the reader through a different method, explaining how to interpret the results and what conclusions can be drawn. The author team showcases key topics, including unsupervised learning, causal inference, spatial weight matrices, spatial econometrics, heterogeneity and bootstrapping. It is accompanied by a suite of data and R code on Github to help readers practise techniques via replication and exercises. This text will be a

• Author : Anindya Banerjee,Juan J. Dolado,John W. Galbraith,David Hendry
• Publisher : Oxford University Press
• Release Date : 1993-05-27
• ISBN : 9780191638916

This book provides a wide-ranging account of the literature on co-integration and the modelling of integrated processes (those which accumulate the effects of past shocks). Data series which display integrated behaviour are common in economics, although techniques appropriate to analysing such data are of recent origin and there are few existing expositions of the literature. This book focuses on the exploration of relationships among integrated data series and the exploitation of these relationships in dynamic econometric modelling. The concepts of

• Author : Ganeshan Wignaraja
• Publisher : Springer
• Release Date : 2015-12-10
• ISBN : 9784431554981

The book provides a comprehensive examination of patterns and determinants of production networks in East Asia, a key driver in the region’s global success. It provides the reader with an accessible understanding of the theoretical literature on production networks and recent developments in empirical analysis at the industry and firm-levels. The topics covered in the book include: gross trade in parts and components and gravity models, trade in value added, industry case studies, and micro data econometric studies of

• Author : Yves Croissant,Giovanni Millo
• Publisher : John Wiley & Sons
• Release Date : 2018-08-10
• ISBN : 9781118949184

Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemiology, this book presents classic methodology and applications as well as more advanced topics and recent developments in this field including error component models, spatial panels and dynamic models. They have developed the software programming in R and host replicable material on the book’s accompanying website.

• Author : Sergio Consoli,Diego Reforgiato Recupero,Michaela Saisana
• Publisher : Springer Nature
• Release Date : 2021
• ISBN : 9783030668914

This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance