Titlebook: Machine Learning for Econometrics and Related Topics; Vladik Kreinovich,Songsak Sriboonchitta,Woraphon Y Book 2024 The Editor(s) (if appli

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,Using Machine Learning Methods to Estimate the Gender Wage Gap,fically, we illustrate how to implement the proposal of Ahrens et al. [.] to use “stacking regression” in combination with the “Double-Debiased Machine Learning” methodology of Chernozhukov et al. [.].

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,The a Priori Procedure (APP) for Estimating Regression Coefficients in Multiple Linear Model with Sded an application to regression coefficients. However, a limitation of the Tong et al. [.] work was the necessity to assume multivariate normality. The present work addresses the limitation with an expansion to multivariate skew normality.

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2198-4182 onomics in agriculture, health, manufacturing, trade, and tr.In the last decades, machine learning techniques – especially techniques of deep learning – led to numerous successes in many application areas, including economics. The use of machine learning in economics is the main focus of this book;

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,Why Shapley Value and Its Variants Are Useful in Machine Learning (and in Other Applications),tural in cooperative games and but not in machine learning. In this paper, we provide a new simple derivation of the Shapley value, a derivation that does not use game-specific requirements like additivity and is, thus, applicable in the machine learning case as well.

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,Portfolio Management of SET50 Stocks Using Deep Reinforcement Learning Methods,cal findings demonstrate that, in terms of Sharpe ratio and cumulative returns, the proposed reinforcement learning technique beats both the SET 50 Average index and the conventional min-variance portfolio allocation strategy.

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Slow-Growing Trees, or diversification. SGT applies the former to estimate a single deep tree, and . (bagging stochastic BT with a high learning rate) uses the latter with additive shallow trees. The performance of this tree ensemble quaternity (Booging, BT, SGT, RF) is assessed on imulated and real regression tasks.

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,Forecasting Market Index of Stock Exchange of Thailand, Malaysia, and Singapore with the Gaussian Pt the Gaussian Process Regression (GPR) is a good model for capturing the stock market index in three Asian stock markets. By testing process, it is shown that the GPR model with lag 1 is the best model for the stock market in Malaysia and Thailand, and the GPR model with lag 5 is suitable for the stock market in Singapore.

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