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Recent development in production risk analyses has raised questions on the conventional approaches to estimating risk preferences. This study proposes to identify the risk separately from input equations with a seminonparametric estimator. The approach circumvents the issue of arbitrary risk specifications. Meanwhile, it facilitates analytical derivation of input equations. The GMM estimation method is then applied to input equations to estimate risk preferences. The procedure is validated by a Monte Carlo experiment. Simulation results show that the proposed method provides a consistent estimator and significantly improves estimation efficiency.

Keywords: Risk Preferences ; GMM ; Simulations ; Seminonparametric Estimator ; Estimation Efficiency

Subject(s): Production Economics

Risk and Uncertainty

Issue Date: 2014

Publication Type: Working or Discussion Paper

PURL Identifier: http://purl.umn.edu/170625

JEL Codes: C14; Q12

Record appears in: Agricultural and Applied Economics Association (AAEA) > 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota





Autor: Wu, Feng ; Guan, Zhengfei

Fuente: http://ageconsearch.umn.edu/record/170625?ln=en







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