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Weak identification with many instruments
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DOI：10.1093/ectj/utae007.png)
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摘要
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Linear instrumental variable regressions are widely used to estimate causal effects. Many instruments arise from the use of 'technical' instruments and more recently from the empirical strategy of 'judge design'. This paper surveys and summarises ideas from recent literature on estimation and statistical inferences with many instruments for a single endogenous regressor. We discuss how to assess the strength of the instruments and how to conduct weak identification robust inference under heteroscedasticity. We establish new results for a jack-knifed version of the Lagrange Multiplier test statistic. Furthermore, we extend the weak identification robust tests to settings with both many exogenous regressors and many instruments. We propose a test that properly partials out many exogenous regressors while preserving the re-centring property of the jack-knife. The proposed tests have correct size and good power properties.
Keyword:
Instrumental variable regressions
many instruments
weak instruments
期刊
IF:
7
论文数:
567
被引数:
2.3K
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Jackknife estimation of a cluster-sample IV regression model with many weak instruments✩具有许多弱仪器的聚类样本IV回归模型的Jackknife估计
PREAI
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