Intercept corrections and structural change
指出，假设数据生成过程恒定不变的预测分析忽视了现实中的结构变化，并比较了差分向量自回归与向量均衡修正机制在应对结构突变时的表现，发现截距修正能改善后者但会增大预测误差方差。
Analyses of forecasting that assume a constant, time-invariant data generating process (DGP), and so implicitly rule out structural change or regime shifts in the economy, ignore an aspect of the real world responsible for some of the more dramatic historical episodes of predictive failure. Some models may offer greater protection against unforeseen structural breaks than others, and various tricks may be employed to robustify forecasts to change. We show that in certain states of nature, vector autoregressions in the differences of the variables (in the spirit of Box-Jenkins time-series modelling), can outperform vector ‘equilibrium-correction’ mechanisms. However, appropriate intercept corrections can enhance the performance of the latter, albeit that reductions in forecast bias may only be achieved at the cost of inflated forecast error variances.