Econometrics with Machine Learning
Editor
- Felix Chan(Curtin University)László Mátyás(Central European University)
Abstract
Individual chapters are listed in the "Chapters" tab
Suggested Citation
DOI: 10.1007/978-3-031-15149-1
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Ölkers, Tim & Liu, Shuang & Mußhoff, Oliver, 2023. "A typology of Malian farmers and their credit repayment performance - An unsupervised machine learning approach," 97th Annual Conference, March 27-29, 2023, Warwick University, Coventry, UK 334547, Agricultural Economics Society - AES.
- Millimet, Daniel L. & Bellemare, Marc, 2023. "Fixed Effects and Causal Inference," IZA Discussion Papers 16202, Institute of Labor Economics (IZA).
- Felix Chan & Les Oxley, 2023. "A pulse check on recent developments in time series econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 3-6, February.
Book Chapters
The following chapters of this book are listed in IDEAS- Felix Chan & László Mátyás, 2022. "Linear Econometric Models with Machine Learning," Advanced Studies in Theoretical and Applied Econometrics, in: Felix Chan & László Mátyás (ed.), Econometrics with Machine Learning, chapter 0, pages 1-39, Springer.
- Felix Chan & Mark N. Harris & Ranjodh B. Singh & Wei (Ben) Ern Yeo, 2022. "Nonlinear Econometric Models with Machine Learning," Advanced Studies in Theoretical and Applied Econometrics, in: Felix Chan & László Mátyás (ed.), Econometrics with Machine Learning, chapter 0, pages 41-78, Springer.
- Robert P. Lieli & Yu-Chin Hsu & Ágoston Reguly, 2022. "The Use of Machine Learning in Treatment Effect Estimation," Advanced Studies in Theoretical and Applied Econometrics, in: Felix Chan & László Mátyás (ed.), Econometrics with Machine Learning, chapter 0, pages 79-109, Springer.
- Marcelo C. Medeiros, 2022. "Forecasting with Machine Learning Methods," Advanced Studies in Theoretical and Applied Econometrics, in: Felix Chan & László Mátyás (ed.), Econometrics with Machine Learning, chapter 0, pages 111-149, Springer.
- William Crown, 2022. "Causal Estimation of Treatment Effects From Observational Health Care Data Using Machine Learning Methods," Advanced Studies in Theoretical and Applied Econometrics, in: Felix Chan & László Mátyás (ed.), Econometrics with Machine Learning, chapter 0, pages 151-175, Springer.
- Oliver Kiss & Gyorgy Ruzicska, 2022. "Econometrics of Networks with Machine Learning," Advanced Studies in Theoretical and Applied Econometrics, in: Felix Chan & László Mátyás (ed.), Econometrics with Machine Learning, chapter 0, pages 177-215, Springer.
- Samuele Centorrino & Jean-Pierre Florens & Jean-Michel Loubes, 2022. "Fairness in Machine Learning and Econometrics," Advanced Studies in Theoretical and Applied Econometrics, in: Felix Chan & László Mátyás (ed.), Econometrics with Machine Learning, chapter 0, pages 217-250, Springer.
- Ekaterina Seregina, 2022. "Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance," Advanced Studies in Theoretical and Applied Econometrics, in: Felix Chan & László Mátyás (ed.), Econometrics with Machine Learning, chapter 0, pages 251-290, Springer.
- Walter Sosa-Escudero & Maria Victoria Anauati & Wendy Brau, 2022. "Poverty, Inequality and Development Studies with Machine Learning," Advanced Studies in Theoretical and Applied Econometrics, in: Felix Chan & László Mátyás (ed.), Econometrics with Machine Learning, chapter 0, pages 291-335, Springer.
- Jantje Sönksen, 2022. "Machine Learning for Asset Pricing," Advanced Studies in Theoretical and Applied Econometrics, in: Felix Chan & László Mátyás (ed.), Econometrics with Machine Learning, chapter 0, pages 337-366, Springer.
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