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Optimal forecasts in the presence of structural breaks. (English) Zbl 1288.62140

Summary: 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.

MSC:

62M20 Inference from stochastic processes and prediction
62P05 Applications of statistics to actuarial sciences and financial mathematics
91G70 Statistical methods; risk measures
91B82 Statistical methods; economic indices and measures

Software:

expsmooth
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References:

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