Estimating Euler equations with noisy data: two exact GMM estimators
利用欧拉方程的结构，提出两种处理测量误差的GMM估计量，蒙特卡洛模拟显示其优于传统方法，并在PSID数据中得到了合理的风险厌恶系数和贴现率估计。
Abstract In this paper we exploit the specific structure of the Euler equation and develop two alternative GMM estimators that deal explicitly with measurement error. The first estimator assumes that the measurement error is log‐normally distributed. The second estimator drops the distributional assumption at the cost of less precision. Our Monte Carlo results suggest that both proposed estimators perform much better than conventional alternatives based on the exact Euler equation or its log‐linear approximation, especially with short panels. An empirical application to the PSID yields plausible and precise estimates of the coefficient of relative risk aversion and the discount rate. Copyright © 2008 John Wiley & Sons, Ltd.