Abstract In a variety of real-world forecasting contexts, researchers have demonstrated that the unweighted average forecast is reasonably accurate and difficult to improve upon with more complex, model-based aggregation methods. We investigate this phenomenon by systematically examining the relationship between individual forecaster characteristics (e.g., bias, consistency) and aspects of the criterion being forecast (e.g., “signal strength”). To this end, we develop a model inspired by Cultural Consensus Theory (Batchelder and Romney, 1988) that (i) allows us to jointly estimate both forecaster characteristics and environmental characteristics and (ii) contains the unweighted average as a special case. This allows us to use the model as a regularization method for forecast aggregation, where restrictions on forecaster parameters make the model similar to use of an unweighted average. Relatedly, the model allows us to apply existing results on optimal forecaster weighting to real data. We show how the model provides guidance for identifying prediction environments where the average forecast can potentially be beaten. We also conduct two simulation studies and illustrate the model’s practical application using forecasts of Australian Football League point spreads.

中文翻译：

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击败平均预测：基于预测者属性的正则化

摘要 在各种现实世界的预测环境中，研究人员已经证明，未加权平均预测相当准确，并且难以通过更复杂的、基于模型的聚合方法进行改进。我们通过系统地检查个体预测者特征（例如偏差、一致性）与被预测标准的各个方面（例如“信号强度”）之间的关系来研究这种现象。为此，我们开发了一个受文化共识理论（Batchelder 和 Romney，1988 年）启发的模型，该模型 (i) 允许我们联合估计预测者特征和环境特征，以及 (ii) 包含未加权平均值作为特例。这允许我们将模型用作预测聚合的正则化方法，其中对预测器参数的限制使模型类似于使用未加权平均值。与此相关的是，该模型允许我们将现有的最佳预测加权结果应用于实际数据。我们展示了该模型如何为识别可能超过平均预测的预测环境提供指导。我们还进行了两项模拟研究，并使用澳大利亚足球联赛积分差的预测来说明该模型的实际应用。