Collaborative Research: Temporal Dependence and Economic Fluctuations -- A Spectral Investigation

这项拟议的研究探索了不可分时间的偏好和技术的经济含义。这项研究的动机是关于时间可分离的常数相对风险厌恶效用函数以及非时间可分离函数的谱效用的经验。频谱效用函数按频率将代理商对消费平稳性的偏好分解为对特定类型平稳性的偏好。低频下的平滑(长期而言)可能或多或少比高频下的平滑更重要。对于时间可分离的首选项，实用程序不随频率变化，但对于非时间可分离的情况，它会随频率变化。这对我们建议研究的各种经济现象都有影响。我们将考虑在偏好中形成习惯的模型如何产生观察到的股权溢价和无风险回报率，以及这种时间上的不可分离性是否可以在解释股票价格的“过度波动”之谜中发挥作用。我们还将分析对动态多样化的影响；当回报序列相关和/或偏好在时间上不可分离时，最优投资组合配置、基金分离和其他结果需要修改。我们预计，像汉森-贾甘纳森界限这样的评估工具的光谱性质将为经济模型的行为提供额外的洞察，即使在偏好是时间可分的情况下也是如此。最后，我们将探讨这些工具是否有助于评估使用构建时间和其他技术的业务周期模型的成功和失败。

The proposed research explores the economic implications of non-time-separable preferences and technology. The research is motivated by experience with spectral utility for time-separable constant-relative-risk-aversion utility functions, as well as non-time-separable functions. Spectral utility functions decompose agents' preferences for consumption smoothness into preferences for particular types of smoothness by frequency. Smoothness at low frequencies (the long run) may be more or less important than smoothness at high frequencies. For time-separable preferences, utility does not vary by frequency, but for non-time-separable cases it does. This carries implications for a variety of economic phenomena that we propose to study. We will consider how models with habit formation in preferences produce observed equity premia and risk-free rates of return and whether such temporal nonseparabilities could play a role in explaining the "excess volatility" puzzle of stock prices. We will also analyze the implications for dynamic diversification; optimal portfolio allocation, fund separation, and other results require modification when returns are serially correlated and/or preferences are temporally nonseparable. We expect that the spectral properties of evaluation devices like the Hansen-Jagannathan bounds will provide additional insight into behavior of economic models even when preferences are time separable. Finally, we will explore whether or not these devices are useful for assessing the successes and failures of business cycle models that use time-to-build and other technologies.