LEADER 04143nam 22006375 450 001 9910592987903321 005 20230909191923.0 010 $a3-031-15149-6 024 7 $a10.1007/978-3-031-15149-1 035 $a(MiAaPQ)EBC7081868 035 $a(Au-PeEL)EBL7081868 035 $a(OCoLC)1344160901 035 $a(CKB)24815140800041 035 $a(DE-He213)978-3-031-15149-1 035 $a(EXLCZ)9924815140800041 100 $a20220907d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEconometrics with Machine Learning /$fedited by Felix Chan, László Mátyás 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (385 pages) 225 1 $aAdvanced Studies in Theoretical and Applied Econometrics,$x2214-7977 ;$v53 311 08$aPrint version: Chan, Felix Econometrics with Machine Learning Cham : Springer International Publishing AG,c2022 9783031151484 320 $aIncludes bibliographical references. 327 $aLinear 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. 330 $aThis book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in ?big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice. . 410 0$aAdvanced Studies in Theoretical and Applied Econometrics,$x2214-7977 ;$v53 606 $aEconometrics 606 $aMachine learning 606 $aMacroeconomics 606 $aEconometrics 606 $aMachine Learning 606 $aQuantitative Economics 606 $aMacroeconomics and Monetary Economics 615 0$aEconometrics. 615 0$aMachine learning. 615 0$aMacroeconomics. 615 14$aEconometrics. 615 24$aMachine Learning. 615 24$aQuantitative Economics. 615 24$aMacroeconomics and Monetary Economics. 676 $a780 676 $a330.028563 702 $aChan$b Felix 702 $aMa?tya?s$b La?szlo? 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910592987903321 996 $aEconometrics with machine learning$93009702 997 $aUNINA