Jackknife instrumental variables estimation in stata

被引:32
|
作者
Poi, Brian P. [1 ]
机构
[1] StataCorp, College Stn, TX 77845 USA
来源
STATA JOURNAL | 2006年 / 6卷 / 03期
关键词
st0108; jive; 2SLS; LIML; AVE; instrumental variables; endogeneity; weak instruments;
D O I
10.1177/1536867X0600600305
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
The two-stage least-squares (2SLS) instrumental variables estimator is commonly used to address endogeneity. However, the estimator suffers from bias that is exacerbated when the instruments are only weakly correlated with the endogenous variables and when many instruments are used. In this article, I discuss jackknife instrumental variables estimation as an alternative to 2SLS. Monte Carlo simulations comparing the jackknife instrument variables estimators to 2SLS and limited information maximum likelihood (LIML) show that two of the four variants perform remarkably well even when 2SLS does not. In a weak-instrument experiment, the two best performing jackknife estimators also outperform LIML.
引用
收藏
页码:364 / 376
页数:13
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