Confidence Intervals for Bias and Size Distortion in IV and Local Projections-IV Models
提出构建两阶段最小二乘估计量偏误和Wald检验规模扭曲的置信区间方法，适用于异方差和序列相关，涵盖局部投影-工具变量模型，通过卡方分布逆推计算，比弱工具变量检验提供更多信息。
In this article, we propose methods to construct confidence intervals for the bias of the two-stage least squares estimator, and the size distortion of the associated Wald test in instrumental variables models with heteroscedasticity and serial correlation. Importantly our framework covers the local projections—instrumental variable model as well. Unlike tests for weak instruments, whose distributions are nonstandard and depend on nuisance parameters that cannot be consistently estimated, the confidence intervals for the strength of identification are straightforward and computationally easy to calculate, as they are obtained from inverting a chi-squared distribution. Furthermore, they provide more information to researchers on instrument strength than the binary decision offered by tests. Monte Carlo simulations show that the confidence intervals have good, albeit conservative, in some cases, small sample coverage. We illustrate the usefulness of the proposed methods in two empirical situations: the estimation of the intertemporal elasticity of substitution in a linearized Euler equation, and government spending multipliers. Supplementary materials for this article are available online.