Forecast Combinations

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Abstract
This article studies two issues in forecast combination, first considering ways to combine forecasts from surveys and time series models. Second, it considers the possibility, advanced by Hendry and Clements (2004), that model instability can help explain the gains in forecasting performance resulting from combination. The article is organized as follows. Section 2 discusses the design of the universe of forecasting models used in combining forecasts from time series models and subjective survey forecasts. Section 3 undertakes an empirical analysis using forecasts from univariate and multivariate linear models, nonlinear models, and survey forecasts. Section 4 provides analytical results that shed light on the performance of forecast combinations under model instability. Section 5 presents empirical results on forecast combinations under breaks. Section 6 concludes.

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