Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions
针对混合频率向量自回归的维度灾难问题，提出一种分层正则化方法，通过优先纳入近期信息系数实现稀疏模式，并稀疏估计误差协方差矩阵以发现即时预测关系，应用于美国经济预测和GDP增长同步指标构建。
Mixed-frequency Vector AutoRegressions (MF-VAR) model the dynamics between variables recorded at different frequencies. However, as the number of series and high-frequency observations per low-frequency period grow, MF-VARs suffer from the “curse of dimensionality.” We curb this curse through a regularizer that permits hierarchical sparsity patterns by prioritizing the inclusion of coefficients according to the recency of the information they contain. Additionally, we investigate the presence of nowcasting relations by sparsely estimating the MF-VAR error covariance matrix. We study predictive Granger causality relations in a MF-VAR for the U.S. economy and construct a coincident indicator of GDP growth. Supplementary materials for this article are available online.