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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Volatility Forecasting in a Data Rich Environmentonal dimension diverges, unless strong restrictions are imposed on the model’s dynamics. In the latter case, the models might become feasible at the expense of reduced economic intuition that can be recovered from the model fit. In turn, this could have a negative impact on the forecast and the identification of its drivers.

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泥土謙卑

Book 2020; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics..

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Entreaty

SLING

Large Bayesian Vector Autoregressionsd considered only small systems with a few variables due to parameter proliferation concern and computational limitations. We first review a variety of shrinkage priors that are useful for tackling the parameter proliferation problem in large Bayesian VARs. This is followed by a detailed discussion

禁令

Volatility Forecasting in a Data Rich Environments literature and on the challenges posed by the increased availability of data. There are limits to the feasibility of all models when the cross-sectional dimension diverges, unless strong restrictions are imposed on the model’s dynamics. In the latter case, the models might become feasible at the e

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Penalized Time Series Regressionied work, namely Ridge Regression, the Least Absolute Shrinkage and Selection Operator (Lasso), the Elastic Net, the adaptive versions of the Lasso as well as Elastic Net and the group Lasso. Other penalties are briefly presented. We discuss theoretical properties such as consistent variable selecti