Econometrics with Machine Learning

Name: Econometrics with Machine Learning
Brand: Springer, Berlin
SKU: 9783031151484
Price: 878.21 RON
Availability: InStock

Autor: László Mátyás, Felix Chan

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Legare: Copertă tare

Editura: Springer, Berlin

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This book helps and promotes the use of machine learning tools and techniques in econometrics and ex...

Informații despre carte

Autor

László Mátyás, Felix Chan

Limbă

engleză

Legare

Carte - Copertă tare

Publicat

2022

Pagini

396

EAN

9783031151484

Enbook ID

39504799

Editura

Springer, Berlin

Greutate

758

Dimensiuni

162 x 28 x 27

Descriere completă

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.

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