Bootstrap Confidence Sets with Weak Instruments
研究了在弱工具变量下构建线性方程内生变量系数置信集的多种方法，提出一种基于t统计量的自助法置信集，发现条件似然比置信集和基于有限信息最大似然估计的自助法置信集在大样本中表现最佳。
We study several methods of constructing confidence sets for the coefficient of the single right-hand-side endogenous variable in a linear equation with weak instruments. Two of these are based on conditional likelihood ratio (CLR) tests, and the others are based on inverting t statistics or the bootstrap P values associated with them. We propose a new method for constructing bootstrap confidence sets based on t statistics. In large samples, the procedures that generally work best are CLR confidence sets using asymptotic critical values and bootstrap confidence sets based on limited-information maximum likelihood (LIML) estimates.