A Nonparametric GMM Series Approach to Solving Multi-equation Asset Pricing Models with Recursive Preferences

37 Pages Posted: 13 May 2019 Last revised: 29 Mar 2021

See all articles by Liyuan Cui

Liyuan Cui

City University of Hong Kong

Yongmiao Hong

Cornell University - Department of Economics

Date Written: May 15, 2016

Abstract

In an exchange economy with recursive preferences (Epstein and Zin, 1989), we propose a novel nonparametric generalized method of moment (GMM) series approach to estimate unknown policy functions which are recursively specified in a system of nonlinear conditional expectation models simultaneously as opposed to sequentially, thereby avoiding accumulation of approximation errors. Unlike current numerical solution methods, this new method does not require the imposition of tight auxiliary assumptions on the conditional distributions or matching moments of state variables and thus avoids spurious model conclusions due to mis-specification errors on state dynamics. Because there is an infinite number of moments and parameters due to series approximations, we propose a series continuously updated estimator (CUE) and establish a new result on consistency and asymptotic normality, which further helps facilitate rigorous inference on general equilibrium models in the presence of misspecified state variables or those with unknown dynamics. Three simulation studies are considered, and our new method has been proven to perform reasonably well in the finite sample in comparison with popular numerical solution methods.

Keywords: CAPM, Epstein and Zin’s model, GMM, price-dividend ratio, recursive preferences, series estimation, simultaneous equation biases, wealth-consumption ratio

JEL Classification: C1, C3, C4, C5, E1, G12

Suggested Citation

Cui, Liyuan and Hong, Yongmiao, A Nonparametric GMM Series Approach to Solving Multi-equation Asset Pricing Models with Recursive Preferences (May 15, 2016). Available at SSRN: https://ssrn.com/abstract=3372147 or http://dx.doi.org/10.2139/ssrn.3372147

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)

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