Econometrics with Machine Learning / / edited by Felix Chan, László Mátyás

• Record Nr.: UNINA9910592987903321
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
• ISBN: 3-031-15149-6
• Edizione: [1st ed. 2022.]
• Descrizione fisica: 1 online resource (385 pages)
• Collana: Advanced Studies in Theoretical and Applied Econometrics, , 2214-7977 ; ; 53
• Disciplina: 780; 330.028563
• Soggetti: Econometrics; Machine learning; Macroeconomics; Machine Learning; Quantitative Economics; Macroeconomics and Monetary Economics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Linear Econometric Models with Machine Learning -- Nonlinear Econometric Models with Machine Learning -- The Use of Machine Learning in Treatment Effect Estimation.-Forecasting with Machine Learning Methods.-Causal Estimation of Treatment Effects From Observational Health Care Data Using Machine Learning Methods -- Econometrics of Networks with Machine Learning -- Fairness in Machine Learning and Econometrics -- Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance -- Poverty, Inequality and Development Studies with Machine Learning -- Machine Learning for Asset Pricing.
• Sommario/riassunto: 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. .