Improving models and forecasts after equilibrium-mean shifts
研究了均衡均值变化（直接或由动态变化诱导）对预测的影响，提出通过乘性指示变量改进模型，从而提升预测准确性。
Equilibrium-mean shifts can result from changes in intercepts with constant dynamics, or be induced by shifts in dynamics with non-zero data means, or both. Induced shifts distort parameter estimates and create a discrepancy between the forecast origin and the equilibrium mean, leading to forecast failure and requiring modifications to previous forecast-error taxonomies. Step-indicator saturation can detect induced shifts, but that does not correct forecast failure. To discriminate direct from induced equilibrium-mean shifts, we augment the model by multiplicative indicators where all selected step indicators interact with the lagged regressand. Forecasts can be markedly improved after induced shifts by including these interactive indicators.