A MONTE CARLO COMPARISON OF VARIOUS ASYMPTOTIC APPROXIMATIONS TO THE DISTRIBUTION OF INSTRUMENTAL VARIABLES ESTIMATORS
通过蒙特卡洛实验比较了三种渐近逼近方法对工具变量估计量分布的适用性，发现常规渐近在样本量较大时表现良好，但工具变量多且相关性极低时失效，而Bekker(1994)方法即使在相关性很小时也表现良好，建议在微观计量应用中报告其置信区间。
We examine empirical relevance of three alternative asymptotic approximations to the distribution of instrumental variables estimators by Monte Carlo experiments. We find that conventional asymptotics provides a reasonable approximation to the actual distribution of instrumental variables estimators when the sample size is reasonably large. Conventional asymptotics fails in this regard only when the number of instruments is large and the correlation between the endogenous regressor and instruments is pathologically small. We find Bekker's (1994) asymptotics provides reasonably good approximation even when the correlation is very small. We conclude that reporting Bekker's (1994) confidence interval would suffice for most microeconometric applications.