每日简报

文字的重要性：利用企业文本数据预测经济下行风险AI 解读

Accurate forecasting of downside risks to economic growth is critically important for policymakers and financial institutions, particularly in the wake of recent economic crises. This paper extends the Growth-at-Risk (GaR) approach by introducing a novel daily sentiment indicator derived from textual analysis of mandatory corporate disclosures (SEC 10-K and 10-Q reports) to forecast downside risks to economic growth. Using the Loughran--McDonald dictionary and a word-count…

多层非线性中断时间序列分析AI 解读

Recent advances in interrupted time series analysis permit characterization of a typical non-linear interruption effect through use of generalized additive models. Concurrently, advances in latent time series modeling allow efficient Bayesian multilevel time series models. We propose to combine these concepts with a hierarchical model selection prior to characterize interruption effects with a multilevel structure, encouraging parsimony and partial pooling while…

区分宏观数据因子模型中的结构突变AI 解读

We develop a projection-based decomposition to disentangle structural breaks in the factor variance and factor loadings. Our approach yields test statistics that can be compared against standard distributions commonly used in the structural break literature. Because standard methods for estimating factor models in macroeconomics normalize the factor variance, they do not distinguish between breaks of the factor variance and factor loadings. Applying our procedure to U.S.…