Titlebook: Econometrics with Machine Learning; Felix Chan,László Mátyás Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive li

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Poverty, Inequality and Development Studies with Machine Learning,iques have been the main contribution in the theoretical arena, whereas taking advantage of the increased availability of new data sources to build or improve the outcome variable has been the main contribution in the empirical front. These inputs would not have been possible without the improvement in computational power.

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Toward a Concrete Logic: Discretastic discount factor and purposefully to test and evaluate existing asset pricing models. Beyond those pertinent applications, machine learning techniques also lend themselves to prediction problems in the domain of empirical asset pricing.

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978-3-031-15151-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Econometrics with Machine Learning978-3-031-15149-1Series ISSN 1570-5811 Series E-ISSN 2214-7977

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Advanced Studies in Theoretical and Applied Econometricshttp://image.papertrans.cn/e/image/301474.jpg

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Martin Gro?,Birgit Hennig,Frank Wallhofftric analysis. Specifically, it examines their applicability in the context of linear regression models. The asymptotic properties of these estimators are discussed and the implications on statistical inference are explored. Given the existing knowledge of these estimators, the chapter advocates the

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