Econometrics with machine learning
Econometrics with machine learning
edited by Felix Chan and Laszlo Matyas
- Switzerland : Springer, ©2022
- xxii, 371 p. : col. ill. ; 24 cm.
- Advanced studies in theoretical and applied econometrics 53 .
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.
9783031151484
Econometrics
Machine Learning
Quantitative Economics
Econometrics -- Data processing
330.015 / CHA-E
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.
9783031151484
Econometrics
Machine Learning
Quantitative Economics
Econometrics -- Data processing
330.015 / CHA-E