不存在未观察到的异质性时的内生性偏差。
Endogeneity bias in the absence of unobserved heterogeneity.
作者信息
Berg Gregory D, Mansley Edward C
机构信息
McKesson Health Solutions, McKesson Corporation, Broomfield, CO 80021, USA.
出版信息
Ann Epidemiol. 2004 Sep;14(8):561-5. doi: 10.1016/j.annepidem.2003.09.020.
PURPOSE
To demonstrate that endogeneity bias can still arise even when no unobserved heterogeneity exists.
METHODS
A formal mathematical proof and a Monte Carlo simulation are used to demonstrate that ordinary estimation techniques will generate biased parameter estimates.
RESULTS
The Monte Carlo results support the formal proof. Even in the absence of unobserved heterogeneity, ordinary least squares estimation that does not account for the endogenous nature of an explanatory variable resulted in a parameter estimate for the endogenous variable that was significantly biased (by a factor of 1.42 for the simple model and 1.98 for the saturated model). Alternatively, controlling for endogeneity using the instrumental variables approach led to an unbiased parameter estimate.
CONCLUSIONS
Endogeneity bias can still occur even when unobserved heterogeneity is not present.
目的
证明即使不存在未观察到的异质性，内生性偏差仍可能出现。
方法
采用形式化数学证明和蒙特卡洛模拟来证明普通估计技术会产生有偏差的参数估计。
结果
蒙特卡洛结果支持形式化证明。即使在不存在未观察到的异质性的情况下，未考虑解释变量内生性质的普通最小二乘法估计也会导致内生变量的参数估计出现显著偏差（简单模型偏差系数为1.42，饱和模型为1.98）。相反，使用工具变量法控制内生性可得到无偏差的参数估计。
结论
即使不存在未观察到的异质性，内生性偏差仍可能发生。
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