Abstract Model averaging for forecasting and mixed estimation is a recognized improved statistical approach. This paper is a first report on: (1). aggregate information from different conditional quantiles within a given model and, (2). model averaging with quantile averaging. Based on a subset of possible methods, we show that aggregating information over different quantiles, with and without combining information across different models, can produce superior forecasts, outperforming forecasts based on conditional mean regressions. We observe a variety of quantile aggregation schemes within a model can significantly improve over forecasts obtained from model combination alone. We provide simulation and empirical evidence. In addition economic value of our proposals is demonstrated within an optimal portfolio decision setting. Higher values of average utility are observed with no exception when an investor employs forecasts which aggregate both within and across model information.

中文翻译：

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股票收益预测的分位数聚合与组合

摘要 用于预测和混合估计的模型平均是公认的改进统计方法。本文是关于： (1) 的首次报道。聚合来自给定模型中不同条件分位数的信息，以及 (2)。模型平均与分位数平均。基于可能的方法的一个子集，我们表明在不同分位数上聚合信息，无论是否结合不同模型的信息，都可以产生卓越的预测，优于基于条件均值回归的预测。我们观察到模型中的各种分位数聚合方案可以显着改善单独从模型组合获得的预测。我们提供模拟和经验证据。此外，我们建议的经济价值在最佳投资组合决策环境中得到体现。