Sources of Entropy in Representative Agent Models
提出了两种基于数据的资产定价模型绩效度量方法，并将其应用于递归效用和习惯模型，通过熵和期限依赖衡量风险溢价与无风险利率，发现模型在产生大的一期熵和小的期限依赖之间存在矛盾。
ABSTRACT We propose two data‐based performance measures for asset pricing models and apply them to models with recursive utility and habits. Excess returns on risky securities are reflected in the pricing kernel's dispersion and riskless bond yields are reflected in its dynamics. We measure dispersion with entropy and dynamics with horizon dependence, the difference between entropy over several periods and one. We compare their magnitudes to estimates derived from asset returns. This exercise reveals tension between a model's ability to generate one‐period entropy, which should be large, and horizon dependence, which should be small.