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多重门槛回归的 Lasso 后推断及收益可预测性应用AI 解读

This article revisits the multiple threshold regression model, wherein coefficient parameters shift across regimes in accordance with the value of a threshold variable. We focus on developing a post-Lasso inference framework to resolve the selection uncertainty—particularly pronounced when threshold effects are weak—intrinsic to the group Lasso-based procedure that jointly estimates the number and locations of multiple thresholds. This objective is accomplished by…

当结构突变遇上门槛效应：结构不稳定下的因子分析AI 解读

Structural instability has been one of the central research questions in economics and finance over many decades. This paper systematically investigates structural instabilities in high dimensional factor models, which portray both structural breaks and threshold effects simultaneously. The observed high dimensional time series are concatenated at an unknown number of break points, while they are described by multiple threshold factor models that are heterogeneous between…

多重门限因子模型的收缩估计AI 解读

This paper proposes a multiple threshold factor model to enhance the flexibility in modeling the underlying regime switching mechanism for high dimensional time series. The factor loadings are assumed to switch between different regimes according to the value of a threshold variable. A novel estimation procedure is proposed to consistently estimate the multiple thresholds with the aid of sorting operation, principal component analysis and shrinkage estimation, which is…

大型因子模型多重结构突变的组融合 LASSO 方法AI 解读

This paper reformulates the identification of multiple structural breaks in factor loadings as a problem of detecting structural breaks in a factor regression, where the estimated pseudo factor corresponding to the largest eigenvalue is regressed on the remaining estimated factors. As a result, a group fused Lasso based estimation procedure is proposed to identify the break dates. Our procedure is practically easy-to-implement with standard statistical packages, overcoming…