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Econometrics with Machine Learning: 53 (Advanced Studies in Theoretical and Applied Econometrics) - Tapa blanda

Libro 52 de 53: Advanced Studies in Theoretical and Applied Econometrics
 
9783031151514: Econometrics with Machine Learning: 53 (Advanced Studies in Theoretical and Applied Econometrics)
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ISBN 10: 3031151518 ISBN 13: 9783031151514
Editorial: Springer, 2023

Sinopsis

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. 

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del autor

László Mátyás is a University Professor at the Department of Economics and Business at the Central European University (CEU – Budapest, Hungary and Vienna, Austria). He (co)authored and (co)edited several high impact publications in econometrics, mostly in the field of panel data. Earlier, among others, he worked as Senior Lecturer at Monash University (Melbourne, Australia), was the founding Director of the Institute for Economic Analysis (Budapest, Hungary), and also served as Provost of CEU. Matyas serves as a co-editor of the Springer book series "Advanced Studies in Theoretical and Applied Econometrics".

Felix Chan is an Associate Professor at Curtin University and an elected Fellow of the Modelling and Simulation Society of Australia and New Zealand (MSSANZ). He serves as the Deputy Head, School of Accounting, Economics and Finance and was the Director of Centre for Research in Applied Economics (CRAE) between 2017 and 2022. Associate Professor Chan had also served as an external consultant to the Commonwealth Grant Commission (CGC), Department of Treasury Western Australia and Chamber of Commerce and Industry (WA) on issues surrounding forecasting, data analytics and mathematical modelling.

De la contraportada

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 furtherand 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.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2023
Idioma
Inglés
ISBN 10 
3031151518
ISBN 13 
9783031151514
Encuadernación
Tapa blanda
Número de edición
1
Número de páginas
396
Editor
Chan Felix, Mtys Lszl
Contacto del fabricante
no disponible
Persona responsable
ProductSafety@springernature.com
ProductSafety@springernature.com

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