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W2131753574 abstract "This paper proposes a new dynamic forecast combination strategy for forecasting ination. The procedure draws on explanations of why the forecast combination puzzle exists and the stylized fact that Phillips curve forecasts of ination exhibit signicant time-variation in forecast accuracy. The forecast combination puzzle is the empirical observation that a simple average of point forecasts is often the best forecasting strategy. The forecast combination puzzle exists because many dynamic weighting strategies tend to shift weights toward Phillips curve forecasts after they exhibit a signicant period of relative forecast improvement, which is often when their forecast accuracy begins to deteriorate. The proposed strategy in this paper weights forecasts according to their expected performance rather than their past performance to anticipate these changes in forecast accuracy. The forward-looking approach is shown to robustly beat equal weights combined and benchmark univariate forecasts of ination in real-time out-of-sample exercises on U.S. and New Zealand ination data." @default.
W2131753574 created "2016-06-24" @default.
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W2131753574 date "2015-01-01" @default.
W2131753574 modified "2023-09-27" @default.
W2131753574 title "Overcoming the Forecast Combination Puzzle: Lessons from the Time-Varying Efficiency of Phillips Curve Forecasts of U.S. Inflation" @default.
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W2131753574 doi "https://doi.org/10.2139/ssrn.2599302" @default.
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