Aureo de Paula – författare

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

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

This volume in Advances in Econometrics showcases fresh methodological and empirical research on the econometrics of networks. Comprising both theoretical, empirical and policy papers, the authors bring together a wide range of perspectives to facilitate a dialogue between academics and practitioners for better understanding this groundbreaking field and its role in policy discussions. This edited collection includes thirteen chapters which covers various topics such as identification of network models, network formation, networks and spatial econometrics and applications of financial networks. Readers can also learn about network models with different types of interactions, sample selection in social networks, trade networks, stochastic dynamic programming in space, spatial panels, survival and networks, financial contagion, spillover effects, interconnectedness on consumer credit markets and a financial risk meter. The topics covered in the book, centered on the econometrics of data and models, are a valuable resource for graduate students and researchers in the field. The collection is also useful for industry professionals and data scientists due its focus on theoretical and applied works.

This volume in Advances in Econometrics showcases fresh methodological and empirical research on the econometrics of networks. Comprising both theoretical, empirical and policy papers, the authors bring together a wide range of perspectives to facilitate a dialogue between academics and practitioners for better understanding this groundbreaking field and its role in policy discussions. This edited collection includes thirteen chapters which covers various topics such as identification of network models, network formation, networks and spatial econometrics and applications of financial networks. Readers can also learn about network models with different types of interactions, sample selection in social networks, trade networks, stochastic dynamic programming in space, spatial panels, survival and networks, financial contagion, spillover effects, interconnectedness on consumer credit markets and a financial risk meter. The topics covered in the book, centered on the econometrics of data and models, are a valuable resource for graduate students and researchers in the field. The collection is also useful for industry professionals and data scientists due its focus on theoretical and applied works.