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摘要翻译：
预测回归中的解释变量通常表现出低信号强度和不同程度的持久性。在这样的背景下，变量的选择是非常重要的。在本文中，我们探讨了LASSO方法在这个预测回归框架中的缺陷和可能性。在存在平稳、局部单位根和协整预测器的情况下，我们证明了自适应LASSO不能渐近消除所有回归系数为零的协整变量。这一新发现激发了一种新的选择后自适应套索，我们称之为双自适应套索(TAlasso)，以恢复变量选择的一致性。TAlasso适应了异构回归子的系统，实现了众所周知的oracle属性。相比之下，传统的LASSO不能同时在所有分量中实现系数估计的一致性和变量筛选。我们应用这些套索方法来评估标准普尔500超额收益的短期和长期可预测性。
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英文标题：
《On LASSO for Predictive Regression》
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作者：
Ji Hyung Lee, Zhentao Shi, Zhan Gao
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最新提交年份：
2021
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分类信息：
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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英文摘要：
Explanatory variables in a predictive regression typically exhibit low signal strength and various degrees of persistence. Variable selection in such a context is of great importance. In this paper, we explore the pitfalls and possibilities of the LASSO methods in this predictive regression framework. In the presence of stationary, local unit root, and cointegrated predictors, we show that the adaptive LASSO cannot asymptotically eliminate all cointegrating variables with zero regression coefficients. This new finding motivates a novel post-selection adaptive LASSO, which we call the twin adaptive LASSO (TAlasso), to restore variable selection consistency. Accommodating the system of heterogeneous regressors, TAlasso achieves the well-known oracle property. In contrast, conventional LASSO fails to attain coefficient estimation consistency and variable screening in all components simultaneously. We apply these LASSO methods to evaluate the short- and long-horizon predictability of S\&P 500 excess returns.
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PDF链接：
https://arxiv.org/pdf/1810.03140
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