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

    Mamatzakis, Emmanuel and Tsionas, M.G. (2019) Revealing forecaster's preferences: a Bayesian multivariate loss function approach. Journal of Forecasting , ISSN 0277-6693.

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    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

    Metadata

    Item Type: Article
    Additional Information: This is the peer reviewed version of the article, which has been published in final form at the link above. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
    School: School of Business, Economics & Informatics > Management
    Depositing User: Emmanuel Mamatzakis
    Date Deposited: 03 Mar 2020 11:13
    Last Modified: 13 Feb 2021 00:02
    URI: https://eprints.bbk.ac.uk/id/eprint/30857

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