Econometrics with Machine Learning

Felix Chan

ISBN 10: 3031151488 / ISBN 13: 9783031151484

Published by Springer, Springer, 2022

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783031151484

Bibliographic Details

• Title: Econometrics with Machine Learning
• Publisher: Springer, Springer
• Publication Date: 2022
• Language: English
• ISBN 10: 3031151488
• ISBN 13: 9783031151484
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 27D millimeters 241 millimeters width by 160 millimeters height by 27 millimeters depth
• Weight: 758 grams

About this title

Synopsis

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. 

About the Author

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.