Inference on matrix-valued factor models under a fixed time horizon
研究了固定时间跨度下矩阵值因子模型的估计与推断，提出增广CS-HAC估计量解决标准误计算问题，并应用于县-行业级经济数据识别出三个因子及其在金融危机和新冠疫情中的不同表现。
This article considers the estimation and inference of matrix-valued factor models under a fixed time horizon. We show that the 2dPCA method maintains consistency and asymptotic normality. However, the conventional Newey-West method becomes infeasible, posing challenges for making inferences. To address this limitation, we introduce an augmented CS-HAC estimator for computing the standard errors. Applying this method to a large set of county-industry-level economic indicators, we identify an aggregate factor, a public sector factor, and a leisure and hospitality factor and show how they are affected differently during the financial crisis and the COVID-19 pandemic.