Forecast Selection and Representativeness

Fotios Petropoulos () and Enno Siemsen ()
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Fotios Petropoulos: School of Management, University of Bath, Bath BA2 7AY, United Kingdom
Enno Siemsen: Wisconsin School of Business, University of Wisconsin–Madison, Madison, Wisconsin 53706

Management Science, 2023, vol. 69, issue 5, 2672-2690

Abstract: Effective approaches to forecast model selection are crucial to improve forecast accuracy and to facilitate the use of forecasts for decision-making processes. Information criteria or cross-validation are common approaches of forecast model selection. Both methods compare forecasts with the respective actual realizations. However, no existing selection method assesses out-of-sample forecasts before the actual values become available—a technique used in human judgment in this context. Research in judgmental model selection emphasizes that human judgment can be superior to statistical selection procedures in evaluating the quality of forecasting models. We, therefore, propose a new way of statistical model selection based on these insights from human judgment. Our approach relies on an asynchronous comparison of forecasts and actual values, allowing for an ex ante evaluation of forecasts via representativeness. We test this criterion on numerous time series. Results from our analyses provide evidence that forecast performance can be improved when models are selected based on their representativeness.

Keywords: forecasting; model selection; model combination; information criteria; representativeness; empirical evaluation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:69:y:2023:i:5:p:2672-2690

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