Estimation and Inference in Two-Step Econometric Models
针对两步估计中因忽略第一步回归误差导致第二步标准误偏误的问题，提出一种简单通用的渐近正确标准误计算方法，并通过两个实例展示其对假设检验的重大影响。
AbstractA commonly used procedure in a wide class of impirical applications is to impute unobserved regressors, such as expectations, from an auxiliary econometric model. This two-step (T-S) procedure fails to account for he fact that imputed regessors are measured with sampling error, so hypothesis tests based on the estimated covariance matrix of the second-step estimator are biased, even in large samples. We present a simple yet general method of calculating asymptotically correct standard errors in T-S models. The proceedure may be applied even when joint estimation methods, such as full information maximum likelihood, are inappropriate or computationally infeasible. We present two examples from recent empirical literature in which these corrections have a major impact on hypothesis testing.