Non- and Semiparametric Techniques for Euler Equations

个人风险感知是任何形式决策的核心，其准确的经验测量是许多经济模型实际适用性的先决条件。对个人风险态度的有效计量经济学评估需要对与最优跨期消费选择相关的欧拉方程中的边际效用进行精确但容易处理的估计。然而，对于这些关键的经济利益因素，可用的标准分析技术依赖于简化模型假设来处理数据挑战，如非平稳消费和未知的效用正确函数形式规范。但在实践中，推动整体结果的往往是这些技术条件，从而产生了各种众所周知的经验谜题，例如对个人风险认知的模糊和矛盾估计的股权溢价谜题。为了避免这样的限制，我们开发了针对这种非标准条件的通用统计技术，旨在获得与实际和经济相关的新见解。特别是，我们的方法不需要预先指定效用函数的参数，而是可以根据数据灵活地确定它们的形式。此外，这些非参数和半参数技术足够普遍，可以在非平稳但经常性消费进入效用水平而不是平稳增长率的情况下进行一致的估计和检验。从这个意义上讲，这些方法是协整类型的。这个项目的重点是半参数模型，它仍然允许灵活的模型拟合，但在非平稳消费的可用样本量下，对纯非参数方法较差的可行性产生了实质性的改善。特别是，我们研究了递归效用规范和Epstein-Zin偏好的估计，对于这些估计，许多校准研究已经显示出有希望的结果。我们期望这样的通用模型类可以显着提高跨期优化模型的实际性能，为目前的一些难题提供新的理解。

Individual risk perception is central to any form of decision making and its accurate empirical measurement is a prerequisite for practical applicability of many economic models. A valid econometric assessment of individual risk attitudes requires precise but tractable estimates of marginal utility in Euler equations associated with optimal intertemporal consumption choice. For these elements of key economic interest, however, available standard analytical techniques depend on simplifying model assumptions to treat data challenges such as nonstationary consumption and unknown correct functional form specification of utility. But in practice, it is often these technical conditions which drive the overall results and have thus produced various well-known empirical puzzles as e.g. the equity premium puzzle with ambiguous and contradicting estimates of individual risk perception. In order to avoid such restrictions, we develop general statistical techniques for such nonstandard conditions aiming to obtain novel insights of practical and economic relevance. In particular, our methods do not require parametric pre-specifications of utility functions but can flexibly determine their form from the data. Furthermore, these non- and semiparametric techniques are sufficiently general to allow for consistent estimation and testing with nonstationary but recurrent consumption entering utility in levels and not in stationary growth rates. In this sense, the methods are of cointegration type. The focus of this project is on semiparametric models which still allow for a flexible model fit but yield substantial improvements to the poor feasibility of pure nonparametric methods in available sample sizes of nonstationary consumption. In particular, we investigate estimation with recursive utility specifications and Epstein-Zin preferences for which many calibration studies have shown promising results. We expect that such general model classes can significantly improve on the practical performance of intertemporal optimization models providing a new understanding of some of the present puzzles.