Flexible Simulated Moment Estimation of Nonlinear Errors-in-Variables Models
提出一种灵活模拟矩估计方法，用于处理微观数据中非线性回归的测量误差问题，并给出渐近方差估计和半参数一致性结果，通过蒙特卡洛研究和恩格尔曲线估计验证其有效性。
Nonlinear regression with measurement error is important for estimation from microeconomic data. One approach to identification and estimation is a causal model, in which the unobserved true variable is predicted by observable variables. This paper details the estimation of such a model using simulated moments and a flexible disturbance distribution. An estimator of the asymptotic variance is given for parametric models. Also, a semiparametric consistency result is given. The value of the estimator is demonstrated in a Monte Carlo study and an application to estimating Engel Curves. © 2001 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology