Identification and inference in linear stochastic discount factor models with excess returns
Identification and inference in linear stochastic discount factor models with excess returns
复制标题
超额收益线性随机贴现因子模型的识别与推理
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Craig Burnside
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文献类型：
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作者：
Craig Burnside
当超额收益用于估计线性随机贴现因子（SDF）模型时，研究人员通常采用将SDF的均值设置为1或将其截距设置为1的标准化SDF。这些标准化通常被视为等同的，但它们在总体和有限样本中都有细微的不同。标准的渐近推断依赖于在两个归一化中不同的秩条件，并且其可能在不同程度上失败。我首先建立，排名条件的失败是一个真正的关注，许多著名的SDF模型在文献中。我还描述了排序条件的失败如何影响总体和有限样本中的推理。我建议使用测试的秩条件，不仅作为一种诊断设备，但也为模型简化。我表明，这个模型简化程序具有理想的性能，在蒙特-卡罗实验与校准模型。
When excess returns are used to estimate linear stochastic discount factor (SDF) models, researchers often adopt a normalization of the SDF that sets its mean to 1, or one that sets its intercept to 1. These normalizations are often treated as equivalent, but they are subtly different both in population, and in finite samples. Standard asymptotic inference relies on rank conditions that differ across the two normalizations, and which can fail to differing degrees. I first establish that failure of the rank conditions is a genuine concern for many well-known SDF models in the literature. I also describe how failure of the rank conditions can affect inference, both in population and in finite samples. I propose using tests of the rank conditions not only as a diagnostic device, but also for model reduction. I show that this model reduction procedure has desirable properties in a Monte-Carlo experiment with a calibrated model.