Articoli correlati a Machine Learning for Econometrics
Sinossi
Machine Learning for Econometrics is a book for economists seeking to grasp modern machine learning techniques - from their predictive performance to the revolutionary handling of unstructured data - in order to establish causal relationships from data.
The volume covers automatic variable selection in various high-dimensional contexts, estimation of treatment effect heterogeneity, natural language processing (NLP) techniques, as well as synthetic control and macroeconomic forecasting. The foundations of machine learning methods are introduced to provide both a thorough theoretical treatment of how they can be used in econometrics and numerous economic applications, and each chapter contains a series of empirical examples, programs, and exercises to facilitate the reader's adoption and implementation of the techniques.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull'autore
Christophe Gaillac is an Associate Professor at the University of Geneva, GSEM. He was a postdoctoral prize research fellow at Oxford University and Nuffield College, and received his PhD in Economics from the Toulouse School of Economics.
Jérémy L'Hour is a quantitative researcher at Capital Fund Management (CFM), a Paris-based systematic hedge fund. He received his PhD in Economics from Université Paris-Saclay.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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MACHINE LEARNING FOR ECONOMETRICS
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Hardcover. Condizione: NEW. International Edition, Brand New, ISBN and Cover same but contents similar to U.S. Edition, We ship from multiple Locations including India. Legal to use despite any disclaimer, We ship to PO , APO and FPO adresses in U.S.A. Ship from multiple Locations including India Choose Expedited Shipping for FASTER DELIVERY.Customer Satisfaction Guaranteed. Codice articolo INISB1#9780198918837
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Machine Learning for Econometrics
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Machine Learning for Econometrics
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Paperback. Condizione: New. Machine Learning for Econometrics is a book for economists seeking to grasp modern machine learning techniques - from their predictive performance to the revolutionary handling of unstructured data - in order to establish causal relationships from data. The volume covers automatic variable selection in various high-dimensional contexts, estimation of treatment effect heterogeneity, natural language processing (NLP) techniques, as well as synthetic control and macroeconomic forecasting. The foundations of machine learning methods are introduced to provide both a thorough theoretical treatment of how they can be used in econometrics and numerous economic applications, and each chapter contains a series of empirical examples, programs, and exercises to facilitate the reader's adoption and implementation of the techniques. Codice articolo LU-9780198918837
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Paperback. Condizione: new. Paperback. Machine Learning for Econometrics is a book for economists seeking to grasp modern machine learning techniques - from their predictive performance to the revolutionary handling of unstructured data - in order to establish causal relationships from data.The volume covers automatic variable selection in various high-dimensional contexts, estimation of treatment effect heterogeneity, natural language processing (NLP) techniques, as wellas synthetic control and macroeconomic forecasting. The foundations of machine learning methods are introduced to provide both a thorough theoretical treatment of how they can be used in econometrics andnumerous economic applications, and each chapter contains a series of empirical examples, programs, and exercises to facilitate the reader's adoption and implementation of the techniques. Machine Learning for Econometrics is a book for economists seeking to grasp modern machine learning techniques - from their predictive performance to the revolutionary handling of unstructured data - in order to establish causal relationships from data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9780198918837
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