Structural Breaks in Grouped Heterogeneity
提出一种新的贝叶斯方法，在存在多个结构断点和未观测的分组异质性时估计面板回归模型，并通过美国20个行业的通胀预测验证了其准确性优于多种流行方法。
Generating accurate forecasts in the presence of structural breaks requires careful management of bias-variance tradeoffs. Forecasting panel data under breaks offers the possibility to reduce parameter estimation error without inducing any bias if there exists a regime-specific pattern of grouped heterogeneity. To this end, we develop a new Bayesian methodology to estimate and formally test panel regression models in the presence of multiple breaks and unobserved regime-specific grouped heterogeneity. In an empirical application to forecasting inflation rates across 20 U.S. industries, our method generates significantly more accurate forecasts relative to a range of popular methods.