NONPARAMETRIC EULER EQUATION IDENTIFICATION AND ESTIMATION
研究了基于消费的资产定价欧拉方程中定价核的非参数识别与估计，首次在低阶条件下证明非参数可识别性，并提出结合核估计与矩阵特征值问题的新估计量，避免逆问题病态性，蒙特卡洛实验显示有限样本表现良好。
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.