论文标题
高维时变系数估计
High-Dimensional Time-Varying Coefficient Estimation
论文作者
论文摘要
在本文中，我们基于高维ITO扩散过程，开发了一种新型的高维时变系数估计方法。为了说明高维时变系数，我们首先使用稀疏条件下使用时间定位的Dantzig选择方案来估计局部（或瞬时）系数，这导致由于正则化而导致局部系数估计值有偏见。为了应对偏见，我们提出了一个偏见方案，该方案提供了表现良好的局部系数估计器。借助局部系数估计值，我们估算了集成系数，并进一步说明了系数过程的稀疏性，我们采用了阈值方案。我们称这种阈值decias为Dantzig（TED）。我们建立了拟议的TED估计量的渐近特性。在经验分析中，我们将TED程序应用于使用高频数据分析高维因子模型。
In this paper, we develop a novel high-dimensional time-varying coefficient estimation method, based on high-dimensional Ito diffusion processes. To account for high-dimensional time-varying coefficients, we first estimate local (or instantaneous) coefficients using a time-localized Dantzig selection scheme under a sparsity condition, which results in biased local coefficient estimators due to the regularization. To handle the bias, we propose a debiasing scheme, which provides well-performing unbiased local coefficient estimators. With the unbiased local coefficient estimators, we estimate the integrated coefficient, and to further account for the sparsity of the coefficient process, we apply thresholding schemes. We call this Thresholding dEbiased Dantzig (TED). We establish asymptotic properties of the proposed TED estimator. In the empirical analysis, we apply the TED procedure to analyzing high-dimensional factor models using high-frequency data.
