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In den WarenkorbPaperback. Zustand: Brand New. 352 pages. 6.69x0.79x9.69 inches. In Stock.
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In den WarenkorbPaperback. Zustand: Brand New. 336 pages. 6.18x0.71x9.25 inches. In Stock.
Sprache: Englisch
Verlag: Oxford University Press Jun 2025, 2025
ISBN 10: 0198918836 ISBN 13: 9780198918837
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware - Machine Learning for Econometrics is a book for economists seeking to grasp modern machine learning techniques - from their predictive performance to the revolutionary handling of unstructured data - in order to establish causal relationships from data. The volume covers automatic variable selection in various high-dimensional contexts, estimation of treatment effect heterogeneity, natural language processing (NLP) techniques, as well as synthetic control and macroeconomic forecasting. The foundations of machine learning methods are introduced to provide both a thorough theoretical treatment of how they can be used in econometrics and numerous economic applications, and each chapter contains a series of empirical examples, programs, and exercises to facilitate the reader's adoption and implementation of the techniques.
Sprache: Englisch
Verlag: Oxford University Press Jan 2026, 2026
ISBN 10: 019897518X ISBN 13: 9780198975182
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware.
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In den WarenkorbHardcover. Zustand: Brand New. 352 pages. 6.89x1.02x9.96 inches. In Stock.
Sprache: Englisch
Verlag: Springer, Berlin|Springer International Publishing|Springer, 2022
ISBN 10: 3031151488 ISBN 13: 9783031151484
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Sprache: Englisch
Verlag: Springer, Berlin|Springer International Publishing|Springer, 2023
ISBN 10: 3031151518 ISBN 13: 9783031151514
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Taschenbuch. Zustand: Neu. Econometrics with Machine Learning | Felix Chan (u. a.) | Taschenbuch | Advanced Studies in Theoretical and Applied Econometrics | xxii | Englisch | 2023 | Springer | EAN 9783031151514 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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In den WarenkorbHardcover. Zustand: Brand New. 336 pages. 6.14x0.75x9.21 inches. In Stock.
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Taschenbuch. Zustand: Neu. 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.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. 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.
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In den WarenkorbPaperback. Zustand: Brand New. 393 pages. 9.25x6.10x0.81 inches. In Stock.
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - In the last decades, machine learning techniques - especially techniques of deep learning - led to numerous successes in many application areas, including economics. The use of machine learning in economics is the main focus of this book; however, the book also describes the use of more traditional econometric techniques. Applications include practically all major sectors of economics: agriculture, health (including the impact of Covid-19), manufacturing, trade, transportation, etc. Several papers analyze the effect of age, education, and gender on economy - and, more generally, issues of fairness and discrimination.We hope that this volume will:help practitioners to become better knowledgeable of the state-of-the-art econometric techniques, especially techniques of machine learning,and help researchers to further develop these important research directions. We want to thank all the authors for their contributions and all anonymous referees for their thorough analysis and helpful comments.
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - In the last decades, machine learning techniques - especially techniques of deep learning - led to numerous successes in many application areas, including economics. The use of machine learning in economics is the main focus of this book; however, the book also describes the use of more traditional econometric techniques. Applications include practically all major sectors of economics: agriculture, health (including the impact of Covid-19), manufacturing, trade, transportation, etc. Several papers analyze the effect of age, education, and gender on economy - and, more generally, issues of fairness and discrimination.We hope that this volume will:help practitioners to become better knowledgeable of the state-of-the-art econometric techniques, especially techniques of machine learning,and help researchers to further develop these important research directions. We want to thank all the authors for their contributions and all anonymous referees for their thorough analysis and helpful comments.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 413,17
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In den WarenkorbHardcover. Zustand: Brand New. 508 pages. 9.25x6.10x9.49 inches. In Stock.
Sprache: Englisch
Verlag: World Scientific Pub Co Inc, 2020
ISBN 10: 9811202389 ISBN 13: 9789811202384
Anbieter: Romtrade Corp., STERLING HEIGHTS, MI, USA
Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
Sprache: Englisch
Verlag: World Scientific Pub Co Inc, 2020
ISBN 10: 9811202389 ISBN 13: 9789811202384
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