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MACHINE LEARNING FOR ECONOMETRICIANS: THE README MANUAL
Marcos López de Prado
The Journal of Financial Data Science Summer 2022, 4 (3) 10-30; DOI:
https://doi.org/10.3905/jfds.2022.1.101
Marcos López de Prado
is global head of quantitative research and development at Abu Dhabi Investment
Authority in Abu Dhabi, UAE, and is a professor of practice at Cornell
University in New York, NY
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ABSTRACT
One of the most exciting recent developments in financial research is the
availability of new administrative, private sector, and micro-level datasets
that did not exist a few years ago. The unstructured nature of many of these
observations, along with the complexity of the phenomena they measure, means
that many of these datasets are beyond the grasp of econometric analysis.
Machine learning (ML) techniques offer the numerical power and functional
flexibility needed to identify complex patterns in a high-dimensional space. ML
is often perceived as a black box, however, in contrast to the transparency of
econometric approaches. In this article, the author demonstrates that each
analytical step of the econometric process has a homologous step in ML analyses.
By clearly stating this correspondence, the author’s goal is to facilitate and
reconcile the adoption of ML techniques among econometricians.
* © 2022 Pageant Media Ltd
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Machine Learning for Econometricians: The Readme Manual
Marcos López de Prado
The Journal of Financial Data Science Jul 2022, 4 (3) 10-30; DOI:
10.3905/jfds.2022.1.101
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* Article
* Abstract
* THE ECONOMETRIC CANON
* HOW SCIENTISTS USE ML
* A ROADMAP FROM ECONOMETRICS TO ML
* CONCLUSION
* APPENDIX A
* ENDNOTES
* REFERENCES
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* PDF
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Learning
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Machine Learning for Econometricians: The Readme Manual
Marcos López de Prado
The Journal of Financial Data Science Jul 2022, 4 (3) 10-30; DOI:
10.3905/jfds.2022.1.101
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