Econometrics with machine learning

Contributor(s):

Material type: Text

9783031151484

Subject(s):

DDC classification:

330.015 CHA-E

Contents:

Chapter 1. Linear Econometric Models with Machine Learning

Chapter 2. Nonlinear Econometric Models with Machine Learning

Chapter 3. The Use of Machine Learning in Treatment Effect Estimation

Chapter 4. Forecasting with Machine Learning Methods

Chapter 5. Causal Estimation of Treatment Effects From Observational Health Care Data Using Machine Learning Methods

Chapter 6. Econometrics of Networks with Machine Learning

Chapter 7. Fairness in Machine Learning and Econometrics

Chapter 8. Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance

Chapter 9. Poverty, Inequality and Development Studies with Machine Learning

Chapter 10. Machine Learning for Asset Pricing

Summary: 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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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds	Course reserves
Books	IIITD Reference	Economics	REF 330.015 CHA-E (Browse shelf(Opens below))	Not for loan		013531		Econometrics with machine learning MNS25

Total holds: 0

Includes bibliographical references.

Chapter 1. Linear Econometric Models with Machine Learning

Chapter 2. Nonlinear Econometric Models with Machine Learning

Chapter 3. The Use of Machine Learning in Treatment Effect Estimation

Chapter 4. Forecasting with Machine Learning Methods

Chapter 5. Causal Estimation of Treatment Effects From Observational Health Care Data Using Machine Learning Methods

Chapter 6. Econometrics of Networks with Machine Learning

Chapter 7. Fairness in Machine Learning and Econometrics

Chapter 8. Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance

Chapter 9. Poverty, Inequality and Development Studies with Machine Learning

Chapter 10. Machine Learning for Asset Pricing

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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