Titlebook: Macroeconomic Forecasting in the Era of Big Data; Theory and Practice Peter Fuleky Book 2020 Springer Nature Switzerland AG 2020 Big Data.M

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Principal Component and Static Factor Analysisn reduction. In this chapter, we consider the forecasting problem using factor models, with special consideration to large datasets. In factor model estimation, we focus on principal component methods, and show how the estimated factors can be used to assist forecasting. Machine learning methods are

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Subspace Methodsace methods are a new class of dimension reduction methods that have been found to yield precise forecasts when applied to macroeconomic and financial data. In this chapter, we review three subspace methods: subset regression, random projection regression, and compressed regression. We provide curre

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Variable Selection and Feature Screeningthe ultra-high dimensionality of the feature space to a moderate size in a fast and efficient way and meanwhile retaining all the important features in the reduced feature space. This is referred to as the sure screening property. After feature screening, more sophisticated methods can be applied to

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Boostingomic researches, especially when the data available is high-dimensional, i.e., the number of explanatory variables (.) is greater than the length of the sample size (.). Common approaches include factor models, the principal component analysis, and regularized regressions. However, these methods req

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Density Forecastinge the accuracy of density forecasts are reviewed and calibration methods for improving the accuracy of forecasts are presented. The manuscript provides some numerical simulation tools to approximate predictive densities with a focus on parallel computing on graphical process units. Some simple examp

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