We consider nonparametric identification and estimation of pricing kernels, or equivalently of marginal utility functions up to scale, in consumption-based asset pricing Euler equations. Ours is the first paper to prove nonparametric identification of Euler equations under low level conditions (without imposing functional restrictions or just assuming completeness). We also propose a novel nonparametric estimator based on our identification analysis, which combines standard kernel estimation with the computation of a matrix eigenvector problem. Our estimator avoids the ill-posed inverse issues associated with nonparametric instrumental variables estimators. We derive limiting distributions for our estimator and for relevant associated functionals. A Monte Carlo experiment shows a satisfactory finite sample performance for our estimators.

中文翻译：

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非参数欧拉方程识别与估计

我们在基于消费的资产定价欧拉方程中考虑定价内核的非参数识别和估计，或等效的边际效用函数的规模化。我们的论文是第一篇在低水平条件下证明欧拉方程的非参数识别的论文（没有施加功能限制或仅假设完整性）。我们还提出了一种基于识别分析的新型非参数估计器，它将标准核估计与矩阵特征向量问题的计算相结合。我们的估计器避免了与非参数工具变量估计器相关的不适定逆问题。我们为我们的估计器和相关的相关泛函推导出限制分布。蒙特卡罗实验显示了我们的估计器令人满意的有限样本性能。