支柱
发表于 2025-3-26 22:35:58
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.
abolish
发表于 2025-3-27 04:39:51
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泥土谦卑
发表于 2025-3-27 06:56:36
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..
巫婆
发表于 2025-3-27 10:18:47
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Occipital-Lobe
发表于 2025-3-27 14:22:31
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Entreaty
发表于 2025-3-27 21:15:13
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SLING
发表于 2025-3-27 22:31:00
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
发表于 2025-3-28 03:59:19
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
Diuretic
发表于 2025-3-28 07:24:02
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发出眩目光芒
发表于 2025-3-28 12:46:05
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