论文标题
通过交叉验证，一致选择面板模型中的组数量
Consistent Selection of the Number of Groups in Panel Models via Cross-Validation
论文作者
论文摘要
小组编号选择是组面板数据建模的关键问题。在这项工作中，我们开发了一种交叉验证（CV）方法来解决此问题。具体而言，我们在时间跨度上将面板数据分为两个数据折叠，并保留针对个体的组结构。我们首先在一个数据折叠上估算组成员资格和参数，然后插入估计值，并利用其他数据折叠来评估设计的标准。随后，通过最小化所有数据折叠的平均标准来估算组号。与现有方法相比，提出的简历方法具有两个优点。首先，该方法完全由数据驱动，因此不涉及进一步的调整参数。其次，该方法可以灵活地应用于各种面板数据模型。从理论上讲，我们通过利用估计算法的优化属性来建立估计一致性。实验使用各种合成数据集和面板模型进行，以进一步说明该方法的优势。最后，通过金融危机，使用了简历方法来分析中国股票市场股票波动的异质模式。
Group number selection is a key problem for group panel data modeling. In this work, we develop a cross-validation (CV) method to tackle this problem. Specifically, we split the panel data into two data folds on the time span, with group structure preserved for individuals. We first estimate the group memberships and parameters on one data fold, then we plug in the estimates and utilize the other data fold to evaluate a designed criterion. Subsequently, the group number is estimated by minimizing the average criterion across all data folds. The proposed CV method has two advantages compared to existing approaches. First, the method is totally data-driven, thus no further tuning parameters are involved. Second, the method can be flexibly applied to a wide range of panel data models. Theoretically, we establish the estimation consistency by taking advantage of the optimization property of the estimation algorithm. Experiments are carried out with a variety of synthetic datasets and panel models to further illustrate the advantages of the proposed method. Lastly, the CV method is employed to analyze the heterogeneous patterns of stock volatilities in the Chinese stock market through the financial crisis.
