Econometrics with Machine Learning

Edited by Felix Chan and Laszlo Matyas ()

in Advanced Studies in Theoretical and Applied Econometrics from Springer, currently edited by Badi Baltagi, Yongmiao Hong, Gary Koop and Walter Krämer

Date: 2022
ISBN: 978-3-031-15149-1
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Chapters in this book:

Ch Chapter 1 Linear Econometric Models with Machine Learning

Felix Chan and Laszlo Matyas

Ch Chapter 10 Machine Learning for Asset Pricing

Jantje Sönksen

Ch Chapter 2 Nonlinear Econometric Models with Machine Learning

Felix Chan, Mark Harris, Ranjodh B. Singh and Wei (Ben) Ern Yeo

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

Robert Lieli, Yu-Chin Hsu and Agoston Reguly

Ch Chapter 4 Forecasting with Machine Learning Methods

Marcelo Medeiros

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

William Crown

Ch Chapter 6 Econometrics of Networks with Machine Learning

Oliver Kiss and Gyorgy Ruzicska

Ch Chapter 7 Fairness in Machine Learning and Econometrics

Samuele Centorrino, Jean-Pierre Florens and Jean-Michel Loubes

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

Ekaterina Seregina

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

Walter Sosa-Escudero, Maria Victoria Anauati and Wendy Brau

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Persistent link: https://EconPapers.repec.org/RePEc:spr:adstae:978-3-031-15149-1

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DOI: 10.1007/978-3-031-15149-1

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