Econometrics machine learning (53 Ergebnisse)

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Paperback. Zustand: 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.…

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Paperback. Zustand: Brand New. 352 pages. 6.69x0.79x9.69 inches. In Stock.

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Taschenbuch. Zustand: Neu. Neuware - 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.…

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Paperback. Zustand: Brand New. 336 pages. 6.18x0.71x9.25 inches. In Stock.

Taschenbuch. Zustand: Neu. Neuware - In an era where geo-located point data has become the backbone of socio-economic, environmental, and urban research, understanding spatial density is crucial. Yet the tools for analysing this data have remained scattered and incomplete. Modelling Spatial Density fills a significant gap by providing a comprehensive, practical, and user-friendly guide to modelling spatial density using cutting-edge quantitative methods. Bridging the worlds of spatial statistics, spatial econometrics, and spatial machine learning, Kopczewska introduces a range of established and novel techniques, made accessible through intuitive explanations, open data, and reproducible R code. Lesser and well-known methods are elegantly combined and discussed in non-mathematical language that is accessible to social scientists. The book makes a significant contribution to the synthesis, development, and application of spatial quantitative methods for spatial density in the social and environmental sciences. Writing for researchers, policymakers, and analysts, the author demystifies complex methods, making them accessible to non-mathematicians while maintaining the rigour expected by specialists. With a focus on practical applications, empirical examples, and actionable insights, this resource empowers readers to turn data into evidence for decision-making. Whether you are exploring urban dynamics, environmental challenges, or socio-economic phenomena, this book provides the essential tools for spatial analysis, bringing clarity and precision to your research.…

Paperback. Zustand: 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…