RT Journal Article
T1 Nonparametric Euler equation identification and estimation
A1 Escanciano, Juan Carlos
A1 Hoderlein, Stefan
A1 Lewbel, Arthur
A1 Linton, Oliver
A1 Srisuma, Sorawoot
AB 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.
PB Cambridge University Press
SN 0266-4666
YR 2020
FD 2020-09-28
LK https://hdl.handle.net/10016/35071
UL https://hdl.handle.net/10016/35071
LA eng
DS e-Archivo
RD 27 jul. 2024