Nearly collinear robust procedures for 2SLS estimation

Alwyn Young
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Abstract

Stata’s two-stage least-squares (2SLS) computation procedures are sensitive to near collinearity among regressors, allowing situations in which reported results depend upon factors as irrelevant as the order of the data and variables. This article illustrates this claim with the public-use data of Oreopoulos (2006, American Economic Review 96: 152–175), where the instrumented coefficient estimate can be made to vary between 0.012 and 30.0 in one specification by permuting the order of the variables. Different methods for improving the accuracy of 2SLS estimates are reviewed, and a Stata command for collinearity-robust 2SLS estimation is provided.
用于 2SLS 估计的近似串联稳健程序
Stata 的两阶段最小二乘法(2SLS)计算程序对回归系数之间的近似共线性非常敏感,这使得报告结果取决于数据和变量顺序等无关因素。本文以 Oreopoulos(2006 年,《美国经济评论》,96: 152-175)的公共使用数据为例说明了这一说法,通过改变变量的顺序,可以使工具系数估计值在一个规范中介于 0.012 和 30.0 之间。本文对提高 2SLS 估计精度的不同方法进行了评述,并提供了一个用于共线性稳健 2SLS 估计的 Stata 命令。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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