A MONTE CARLO COMPARISON OF VARIOUS ASYMPTOTIC APPROXIMATIONS TO THE DISTRIBUTION OF INSTRUMENTAL VARIABLES ESTIMATORS
A MONTE CARLO COMPARISON OF VARIOUS ASYMPTOTIC APPROXIMATIONS TO THE DISTRIBUTION OF INSTRUMENTAL VARIABLES ESTIMATORS
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工具变量估计量分布的各种渐近近似的蒙特卡罗比较
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
A. Inoue
中科院分区：
文献类型：
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作者：
J. Hahn;A. Inoue
摘要我们通过蒙特卡罗实验检验了三种替代渐近近似对工具变量估计量分布的经验相关性。我们发现，传统的渐近提供了一个合理的近似工具变量估计量的实际分布时，样本量是相当大的。对于大多数样本量，我们发现Bekker[11]渐近提供了相当好的近似，即使第一阶段R2非常小。我们得出结论，报告Bekker[11]置信区间足以满足大多数微观计量经济学（横截面）应用，Staiger和Stock[5]渐近近似的比较优势是在宏观计量经济学（时间序列）应用中典型的样本量应用。
ABSTRACT 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. For most sample sizes, we find Bekker[11] asymptotics provides reasonably good approximation even when the first stage R 2 is very small. We conclude that reporting Bekker[11] confidence interval would suffice for most microeconometric (cross-sectional) applications, and the comparative advantage of Staiger and Stock[5] asymptotic approximation is in applications with sample sizes typical in macroeconometric (time series) applications.