Optimal Forecasts in the Presence of Structural Breaks
Optimal Forecasts in the Presence of Structural Breaks
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DOI：
10.2139/ssrn.1977191
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发表时间：
2011-10
期刊：
影响因子：
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通讯作者：
M. Pesaran;A. Pick;M. Pranovich
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文献类型：
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作者：
M. Pesaran;A. Pick;M. Pranovich
本文考虑了连续和离散结构突变下的预测问题，并提出加权观测值以获得MSFE意义下的最优预测。我们得到最佳的权重提前一步预测。在连续中断下，我们的方法在很大程度上恢复了指数平滑权重。在离散突变下，我们给出了具有单个回归变量的模型的最优权重的解析表达式，以及具有多个回归变量的模型的渐近有效权重。它表明，在这些情况下，最佳的权重是相同的，在一个给定的制度内的观察和不同的制度。在实践中，结构突变的信息是不确定的，提出了一种基于鲁棒最优权重的预测方法。我们提出的方法的相对性能进行了研究，使用Monte Carlo实验和实证应用预测真实的GDP使用的收益率曲线在9个工业经济体。
This paper considers the problem of forecasting under continuous and discrete structural breaks and proposes weighting observations to obtain optimal forecasts in the MSFE sense. We derive optimal weights for one step ahead forecasts. Under continuous breaks, our approach largely recovers exponential smoothing weights. Under discrete breaks, we provide analytical expressions for optimal weights in models with a single regressor, and asymptotically valid weights for models with more than one regressor. It is shown that in these cases the optimal weight is the same across observations within a given regime and differs only across regimes. In practice, where information on structural breaks is uncertain, a forecasting procedure based on robust optimal weights is proposed. The relative performance of our proposed approach is investigated using Monte Carlo experiments and an empirical application to forecasting real GDP using the yield curve across nine industrial economies.