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NONPARAMETRIC EULER EQUATION IDENTIFICATION AND ESTIMATION
Published online by Cambridge University Press: 28 September 2020
Abstract
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.
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- © The Author(s), 2020. Published by Cambridge University Press
Footnotes
We thank Don Andrews, Bob Becker, Xiaohong Chen, anonymous referees, and seminar participants at University of Miami, UC San Diego, joint MIT-Harvard, Semiparametric Methods in Economics and Finance Workshop (London, 2010), Cowles workshop (2010), AMES (Seoul, 2011), CFE (London, 2013), and the conference in honor of Don Andrews (Konstanz, 2015) for helpful comments. All errors are our own. This paper replaces “Nonparametric Euler Equation Identification and Estimation,” by Lewbel and Linton (2010), and by Lewbel, Linton, and Srisuma (2012), and replaces “Nonparametric Identification of Euler Equations,” by Escanciano and Hoderlein (2010, 2012).
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