Multiple Structural Breaks in Interactive Effects Panel Data Models
开发了新的计量方法，用于检测交互固定效应面板数据模型中的多重结构断点，并应用于美国银行面板数据，发现量化宽松政策未能有效刺激银行信贷。
ABSTRACT This paper develops new econometric methods for multiple structural break detection in panel data models with interactive fixed effects. The new methods include tests for the presence of structural breaks, estimators for the number of breaks and their location, and a method for constructing asymptotically valid break date confidence intervals. The new methodology is applied to a large panel of US banks for a period characterized by massive quantitative easing programs aimed at lessening the impact of the global financial crisis and the COVID‐19 pandemic. The question we ask is as follows: Have these programs been successful in spurring bank lending in the US economy? The short answer turns out to be: “No”.