Solving Euler Equations via Two-Stage Nonparametric Penalized Splines

52 Pages Posted: 14 May 2019 Last revised: 30 Apr 2020

See all articles by Liyuan Cui

Liyuan Cui

City University of Hong Kong

Yongmiao Hong

Cornell University - Department of Economics

Yingxing Li

Xiamen University

Date Written: May 1, 2018

Abstract

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

Suggested Citation

Cui, Liyuan and Hong, Yongmiao and Li, Yingxing, Solving Euler Equations via Two-Stage Nonparametric Penalized Splines (May 1, 2018). Journal of Econometrics, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3372148 or http://dx.doi.org/10.2139/ssrn.3372148

Liyuan Cui (Contact Author)

City University of Hong Kong ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

Yongmiao Hong

Cornell University - Department of Economics ( email )

Department of Statistical Science
414 Uris Hall
Ithaca, NY 14853-7601
United States
607-255-5130 (Phone)
607-255-2818 (Fax)

Yingxing Li

Xiamen University ( email )

Xiamen, Fujian 361005
China

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