Non- and Semiparametric Techniques for Euler Equations

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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.