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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https://doi.org/10.1007/978-3-031-51183-7data when a large set of predictors is available and the target variable is a scalar. We start by defining the forecasting scheme setup as well as different approaches to compare forecasts generated by different models/methods. More specifically, we review three important techniques to compare forec

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Linkage Disequilibrium Mapping Concepts,g economic theory but are also helpful in examining their applications in empirical analyses. This has been particularly the case recently as data associated with networks are often readily available. While researchers may have access to real-world network structured data, in many cases, their volum

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,Log-Arithmetic, with Single and Dual Base,gates individual characteristics of the observations of the learning sample. But this information aggregation does not consider any potential selection on unobservables and any status quo biases which may be contained in the training sample. The latter bias has raised concerns around the so-called .

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Assortment and Merchandising Strategylopment (PID) studies. It proposes a novel taxonomy to classify the contributions of ML methods and new data sources used in this field. Contributions lie in two main categories. The first is making available better measurements and forecasts of PID indicators in terms of frequency, granularity, and

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Toward a Concrete Logic: Discreta additional benefits that machine learning – in addition to, or in combination with, standard econometric approaches – can bring to the table. This issue is of particular importance because in recent years, improved data availability and increased computational facilities have had huge effects on fi

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Linear Econometric Models with Machine Learning,tric 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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Forecasting with Machine Learning Methods,data when a large set of predictors is available and the target variable is a scalar. We start by defining the forecasting scheme setup as well as different approaches to compare forecasts generated by different models/methods. More specifically, we review three important techniques to compare forec