Revealing forecaster's preferences: A Bayesian multivariate loss function approach

Emmanuel Mamatzakis and Mike Tsionas

Journal of Forecasting, 2020, vol. 39, issue 3, 412-437

Abstract: Revealing the underlying preferences of a forecaster has always been at the core of much controversy. Herein, we build on the multivariate loss function concept and propose a flexible and multivariate family of likelihoods. This allows examining whether a vector of forecast errors, along with control variables, shapes a forecaster's preferences and, therefore, the underlying multivariate, nonseparable, loss function. We estimate the likelihood function using Bayesian exponentially tilted empirical likelihood, which reveals the shape of the parameter and the power of the multivariate loss function. In the empirical section, the reported evidence reveals that the EU Commission forecasts are predominantly asymmetric, leaning towards optimism in the year ahead, while a correction towards pessimism occurs in the current year forecast. There is some variability of this asymmetry across member states, with forecasts, i.e. gross domestic product growth, for large Member States exhibiting more optimism

Date: 2020
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https://doi.org/10.1002/for.2636

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:39:y:2020:i:3:p:412-437

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