摘要翻译：
我们开发了一种新的“解耦-再耦”动态预测策略，并为在数据丰富的环境中预测和经济决策的文献做出了贡献。在这个框架下，一组预测器以预测密度的形式产生不同的潜在状态，然后在隐含的时变潜在因子模型中合成这些状态。结果，跨预测密度和偏差的潜在相互依赖关系被顺序地学习和校正。与稀疏建模和变量选择过程不同，我们不先验地假设存在一个给定的主动预测子子集，该子集表征了一个兴趣量的预测密度。我们通过调查一大组财务比率和宏观经济变量对不同行业股票溢价和美国通货膨胀率的预测内容来检验我们的程序，这两个变量是金融和宏观经济学中的两个热点。我们发现，我们的预测综合框架在保持预测变量的可解释性的同时，产生了统计和经济上显著的样本外效益。此外，主要的实证结果表明，我们提出的框架优于套索型收缩回归、基于因子的降维、序列变量选择和等权线性池方法。
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英文标题：
《Large-Scale Dynamic Predictive Regressions》
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作者：
Daniele Bianchi and Kenichiro McAlinn
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最新提交年份：
2018
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要：
  We develop a novel "decouple-recouple" dynamic predictive strategy and contribute to the literature on forecasting and economic decision making in a data-rich environment. Under this framework, clusters of predictors generate different latent states in the form of predictive densities that are later synthesized within an implied time-varying latent factor model. As a result, the latent inter-dependencies across predictive densities and biases are sequentially learned and corrected. Unlike sparse modeling and variable selection procedures, we do not assume a priori that there is a given subset of active predictors, which characterize the predictive density of a quantity of interest. We test our procedure by investigating the predictive content of a large set of financial ratios and macroeconomic variables on both the equity premium across different industries and the inflation rate in the U.S., two contexts of topical interest in finance and macroeconomics. We find that our predictive synthesis framework generates both statistically and economically significant out-of-sample benefits while maintaining interpretability of the forecasting variables. In addition, the main empirical results highlight that our proposed framework outperforms both LASSO-type shrinkage regressions, factor based dimension reduction, sequential variable selection, and equal-weighted linear pooling methodologies.
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PDF链接：
https://arxiv.org/pdf/1803.06738