Estimation and Inference in Two-Step Econometric Models
针对两步估计法中忽略插补回归变量抽样误差导致检验偏误的问题，提出一种计算渐近正确标准误的通用方法，适用于联合估计不可行或计算困难的情形。
Abstract A commonly used procedure in a wide class of empirical applications is to impute unobserved regressors, such as expectations, from an auxiliary econometric model. This two-step (T-S) procedure fails to account for the fact that imputed regressors 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 procedure 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.