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Formalizing a Postprocessing Procedure for Linear-Convex Combination Forecasts
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AI
DOI：10.1002/for.3229.png)
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We investigate mean squared forecast error (MSE) accuracy improvements for linear-convex combination forecasts, whose components are pretreated by a postprocessing procedure called vector autoregressive forecast error modeling (VAFEM). Assuming that the forecast error series of the individual forecasts are governed by a stable VAR process under classic conditions, we obtain the following results: (i) VAFEM treatment bias corrects all individual and linear-convex combination forecasts. (ii) Any VAFEM-treated combination has a smaller theoretical MSE than its untreated analog, if the VAR parameters are known. (iii) In empirical applications, VAFEM gains depend on (1) in-sample sizes, (2) out-of-sample forecast horizons, and (3) the biasedness of the untreated forecast combination. We demonstrate the VAFEM capacity in simulations and for realized-volatility forecasting, using S&P 500 data.
Keyword:
combination forecasts
mean squared error loss
multivariate least squares estimation
VAR forecast error modeling
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2.7
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2.3K
被引数:
3.0K
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