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# Consistent Estimation with a Large Number of Weak Instruments

## Author

Listed:
• John C. Chao
• Norman R. Swanson

## Abstract

This paper analyzes the conditions under which consistent estimation can be achieved in instrumental variables (IV) regression when the available instruments are weak and the number of instruments, K n , goes to infinity with the sample size. We show that consistent estimation depends importantly on the strength of the instruments as measured by r n , the rate of growth of the so-called concentration parameter, and also on K n . In particular, when K n →∞, the concentration parameter can grow, even if each individual instrument is only weakly correlated with the endogenous explanatory variables, and consistency of certain estimators can be established under weaker conditions than have previously been assumed in the literature. Hence, the use of many weak instruments may actually improve the performance of certain point estimators. More specifically, we find that the limited information maximum likelihood (LIML) estimator and the bias-corrected two-stage least squares (B2SLS) estimator are consistent when $\sqrt{K_{n}}/r_{n}\rightarrow 0$ K n / r n → 0 , while the two-stage least squares (2SLS) estimator is consistent only if K n /r n →0 as n→∞. These consistency results suggest that LIML and B2SLS are more robust to instrument weakness than 2SLS. Copyright The Econometric Society 2005.

## Suggested Citation

• John C. Chao & Norman R. Swanson, 2005. "Consistent Estimation with a Large Number of Weak Instruments," Econometrica, Econometric Society, vol. 73(5), pages 1673-1692, September.
• Handle: RePEc:ecm:emetrp:v:73:y:2005:i:5:p:1673-1692
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## References listed on IDEAS

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## More about this item

### JEL classification:

• C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
• C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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