Solving Euler Equations via Two-Stage Nonparametric Penalized Splines
52 Pages Posted: 14 May 2019 Last revised: 30 Apr 2020
Date Written: May 1, 2018
This study proposes a novel nonparametric estimation approach to solving asset-pricing models. Our method is robust to misspecification errors and it inherits a closed-form solution that facilitates ease of implementation. By transforming the Euler equation, our estimate is fully identified, and we establish large sample properties of the proposed estimate for a broad class of stationary Markov state variables. Using the merit of penalized splines, we design a fast data-based algorithm to e↵ectively tune the smoothing parameter. Our approach exhibits superior performance even with a small sample size. For application, using US data from 1947 to 2017, we reinvestigate the return predictability and find that high implied dividend yield, obtained from our misspecification-free approach, significantly predicts lower future cash flows and higher interest rates at short horizons.
Keywords: Euler equation, implied price-dividend ratio, nonparametric, penalized splines, two-stage regression, return predictability.
JEL Classification: C1, C3, C4, C5, E1, G12
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