000			03153nam a22003857a 4500
003			IIITD
005			20250716174033.0
008			250710b |||||||| |||| 00| 0 eng d
020			_a9783031151484
040			_aIIITD
082			_a330.015 _bCHA-E
245			_aEconometrics with machine learning _cedited by Felix Chan and Laszlo Matyas
260			_aSwitzerland : _bSpringer, _c©2022
300			_axxii, 371 p. : _bcol. ill. ; _c24 cm.
490			_aAdvanced studies in theoretical and applied econometrics _v53
504			_aIncludes bibliographical references.
505			_tChapter 1. Linear Econometric Models with Machine Learning
505			_tChapter 2. Nonlinear Econometric Models with Machine Learning
505			_tChapter 3. The Use of Machine Learning in Treatment Effect Estimation
505			_tChapter 4. Forecasting with Machine Learning Methods
505			_tChapter 5. Causal Estimation of Treatment Effects From Observational Health Care Data Using Machine Learning Methods
505			_tChapter 6. Econometrics of Networks with Machine Learning
505			_tChapter 7. Fairness in Machine Learning and Econometrics
505			_tChapter 8. Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance
505			_tChapter 9. Poverty, Inequality and Development Studies with Machine Learning
505			_tChapter 10. Machine Learning for Asset Pricing
520			_aThis 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.
650			_aEconometrics
650			_aMachine Learning
650			_aQuantitative Economics
650			_aEconometrics -- Data processing
700			_aChan, Felix _eeditor
700			_aMatyas, Laszlo _eeditor
942			_cBK _2ddc
999			_c209191 _d209191