Empirically-transformed linear opinion pools

Anthony Garratt, Timo Henckel and Shaun Vahey

International Journal of Forecasting, 2023, vol. 39, issue 2, 736-753

Abstract: The linear opinion pool (LOP) produces potentially non-Gaussian combination forecast densities. In this paper, we propose a computationally convenient transformation for the LOP to mirror the non-Gaussianity exhibited by the target variable. Our methodology involves a Smirnov transform to reshape the LOP combination forecasts using the empirical cumulative distribution function. We illustrate our empirically transformed opinion pool (EtLOP) approach with an application examining quarterly real-time forecasts for U.S. inflation evaluated on a sample from 1990:1 to 2020:2. EtLOP improves performance by approximately 10% to 30% in terms of the continuous ranked probability score across forecasting horizons.

Keywords: Density forecast combination; Linear opinion pool; Smirnov transform; Inflation (search for similar items in EconPapers)
Date: 2023
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Working Paper: Empirically-transformed linear opinion pools (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:39:y:2023:i:2:p:736-753

DOI: 10.1016/j.ijforecast.2022.02.003

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