Efficient Estimation of Risk Preferences
提出一种半非参数估计程序，无需分布假设即可估计风险密度，并通过数值积分构造条件矩的闭式表达式，相比传统GMM方法在蒙特卡洛模拟中效率显著提升。
Abstract 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.