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Efficient Estimation of Risk Preferences
Author(s) -
Wu Feng,
Guan Zhengfei
Publication year - 2018
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.1093/ajae/aay015
Subject(s) - estimator , estimation , econometrics , construct (python library) , preference , generalized method of moments , computer science , mathematical optimization , monte carlo method , moment (physics) , conditional expectation , mathematics , statistics , economics , physics , management , classical mechanics , programming language
Risk and the risk attitude of agents are two fundamental elements of decision making under risk and uncertainty. Recent developments in risk and risk preference analyses have raised questions on conventional approaches to estimating risk preferences. This study proposes an estimation procedure that employs a seminonparametric estimator to estimate the density function of risk without imposing distributional assumptions, as well as a numerical integration method to construct closed‐form expressions of conditional moment conditions for efficient estimation. The method achieves a substantial efficiency improvement relative to the conventional GMM approach in Monte Carlo simulations. The proposed approach is general and applies to the estimation of behavioral choice models under risk, or models that require expectation operations and closed‐form equations for estimation.