A Conditional Likelihood Ratio Test for Structural Models
提出一种基于条件分布构造精确相似检验的通用方法，适用于正态误差且简化型协方差矩阵已知的联立方程模型。当协方差矩阵需估计且误差非正态时，该检验在弱工具变量渐近下仍保持相似性。基于似然比统计量的条件检验简单且功效良好，在弱识别时优于得分检验。
This paper develops a general method for constructing exactly similar tests based on the conditional distribution of nonpivotal statistics in a simultaneous equations model with normal errors and known reduced-form covariance matrix. These tests are shown to be similar under weak-instrument asymptotics when the reduced-form covariance matrix is estimated and the errors are non-normal. The conditional test based on the likelihood ratio statistic is particularly simple and has good power properties. Like the score test, it is optimal under the usual local-to-null asymptotics, but it has better power when identification is weak.