Noncommon Breaks
提出一种新的贝叶斯方法估计面板回归模型中的非共同结构断点，允许不同截面在不同时间受到冲击，并应用于国际股票收益可预测性，相比基准方法显著提升预测精度，为风险厌恶投资者带来经济效用增益。
We develop a new Bayesian approach to estimate noncommon structural breaks in panel regression models. Any subset of the cross-section may be hit at different times within a break window. Break-specific parameters are learned from the cross-section. They reflect whether (i) breaks hit many or few series and (ii) there is a long or short lag between the first and final series hit by a break. In an empirical application to international stock return predictability, the method generates significantly more accurate forecasts than several benchmarks that yield economically meaningful utility gains for a risk averse investor with power utility.