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Measuring Uncertainty of a Combined Forecast and Some Tests for Forecaster Heterogeneity*

Kajal Lahiri (University at Albany, SUNY, Albany, USA)
Huaming Peng (Rensselaer Polytechnic Institute, Troy, USA)
Xuguang Simon Sheng (American University, Washington, USA)

Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling

ISBN: 978-1-80262-062-7, eISBN: 978-1-80262-061-0

Publication date: 18 January 2022

Abstract

From the standpoint of a policy maker who has access to a number of expert forecasts, the uncertainty of a combined or ensemble forecast should be interpreted as that of a typical forecaster randomly drawn from the pool. This uncertainty formula should incorporate forecaster discord, as justified by (i) disagreement as a component of combined forecast uncertainty, (ii) the model averaging literature, and (iii) central banks’ communication of uncertainty via fan charts. Using new statistics to test for the homogeneity of idiosyncratic errors under the joint limits with both T and n approaching infinity simultaneously, the authors find that some previously used measures can significantly underestimate the conceptually correct benchmark forecast uncertainty.

Keywords

Citation

Lahiri, K., Peng, H. and Sheng, X.S. (2022), "Measuring Uncertainty of a Combined Forecast and Some Tests for Forecaster Heterogeneity*", Chudik, A., Hsiao, C. and Timmermann, A. (Ed.) Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling (Advances in Econometrics, Vol. 43A), Emerald Publishing Limited, Bingley, pp. 29-50. https://doi.org/10.1108/S0731-90532021000043A003

Publisher

:

Emerald Publishing Limited

Copyright © 2022 Kajal Lahiri, Huaming Peng and Xuguang Simon Sheng