Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors
Abstract: We estimate uncertainty measures for point forecasts obtained from survey data, pooling information embedded in observed forecast errors for different forecast horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon specification of stochastic volatility. We apply our method to forecasts for various macroeconomic variables from the Survey of Professional Forecasters. Compared to constant variance approaches, our stochastic volatility model improves the accuracy of uncertainty measures for survey forecasts. Our method can also be applied to other surveys like the Blue Chip Consensus, or the Federal Open Market Committee?s Summary of Economic Projections.
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Provider: Federal Reserve Bank of Cleveland
Part of Series: Working Papers
Publication Date: 2017-09-01
Note: This paper is a revision of Working Paper 17-15 published in September of 2017.