Reduced forms and weak instrumentation
推导了简化形式估计量和预测量的精确有限样本与渐近分布，允许结构方程未识别或弱识别，发现部分受限简化形式预测的均方误差远小于非受限形式，对弱工具变量下的预测有重要指导意义。
This paper develops exact finite sample and asymptotic distributions for a class of reduced form estimators and predictors, allowing for the presence of unidentified or weakly identified structural equations. Weak instrument asymptotic theory is developed directly from finite sample results, unifying earlier findings and showing the usefulness of structural information in making predictions from reduced form systems in applications. Asymptotic results are reported for predictions from models with many weak instruments. Of particular interest is the finding that, in unidentified and weakly identified structural models, partially restricted reduced form predictors have considerably smaller forecast mean square errors than unrestricted reduced forms. These results are related to the use of shrinkage methods in system-wide reduced form estimation.