当仪器多或弱时的线性模型IV估计

Q3 Mathematics
Michael P. Murray
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引用次数: 6

摘要

经济学家经常依靠工具变量估计来克服内源性解释变量在普通最小二乘估计中引起的偏差。然而,传统的工具变量估计,如两阶段最小二乘和有限信息最大似然估计,即使在非常大的样本中,如果使用的工具数量很大或仅与内生解释变量弱相关,也会遭受持续的估计量偏差和检验大小偏差。本文回顾了在线性回归模型中处理弱工具和大量工具的策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Linear Model IV Estimation When Instruments Are Many or Weak
Abstract Economists rely frequently on instrumental variables estimation to overcome biases that endogenous explanatory variables cause in ordinary least squares estimation. However, traditional instrumental variables estimators, such as two-stage least squares and limited information maximum likelihood estimation, can suffer persistent estimator biases and size-of-test biases in even very large samples if the instruments used are large in number or are only weakly correlated with an endogenous explanatory variable. This paper reviews strategies for grappling with weak instruments and with large numbers of instruments in linear regression models.
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来源期刊
Journal of Econometric Methods
Journal of Econometric Methods Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
2.20
自引率
0.00%
发文量
7
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