Optimal Forecasts from Markov Switching Models
推导了马尔可夫转换模型在均方预测误差意义下的最优预测，通过加权观测实现，并分析了状态不确定性对预测的影响，模拟和实际数据表明最优权重能显著降低预测误差。
We derive forecasts for Markov switching models that are optimal in the MSFE sense by means of weighting observations. We provide analytic expressions of the weights conditional on the Markov states and conditional on state probabilities. This allows us to study the effect of uncertainty around states on forecasts. It emerges that, even in large samples, forecasting performance increases substantially when the construction of optimal weights takes uncertainty around states into account. Performance of the optimal weights is shown through simulations and an application to US GNP, where using optimal weights leads to significant reductions in MSFE.