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Some Further Results on the Exact Small Sample Properties of the Instrumental Variable Estimator
Abstract
New results on the exact small sample distribution of the instrumental variable estimator are presented by studying an important special case. The exact closed forms for the probability density and cumulative distribution functions are given. There are a number of surprising findings. The small sample distribution is bimodal. with a point of zero probability mass. As the asymptotic variance grows large, the true distribution becomes concentrated around this point of zero mass. The central tendency of the estimator may be closer to the biased least squares estimator than it is to the true parameter value. The first and second moments of the IV estimator are both infinite. In the case in which least squares is biased upwards, and most of the mass of the IV estimator lies to the right of the true parameter, the mean of the IV estimator is infinitely negative. The difference between the true distribution and the normal asymptotic approximation depends on the ratio of the asymptotic variance to a parameter related to the correlation between the regressor and the regression, error. In particular, when the instrument is poorly correlated with the regressor, the asymptotic approximation to the distribution of the instrumental variable estimator will not be very accurate.
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Cited by
Initial conditions and moment restrictions in dynamic panel data models
Blundell, R., & Bond, S. (1998) · Journal of Econometrics · doi:10.1016/s0304-4076(98)00009-8 · Cited by 22,571
A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments
Stock, J. H., Wright, J. H., & Yogo, M. (2002) · Journal of Business & Economic Statistics · doi:10.1198/073500102288618658 · Cited by 4,274
GMM Estimation with persistent panel data: an application to production functions
Blundell, R., & Bond, S. (2000) · Econometric Reviews · doi:10.1080/07474930008800475 · Cited by 1,625
Using multiple genetic variants as instrumental variables for modifiable risk factors
Palmer, T. M., Lawlor, D. A., Harbord, R. M., Sheehan, N. A., Tobias, J. H., Timpson, N. J., Smith, G. D., & Sterne, J. A. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280210394459 · Cited by 1,289
Controlling for endogeneity with instrumental variables in strategic management research
Bascle, G. (2008) · Strategic Organization · doi:10.1177/1476127008094339 · Cited by 1,133
Metadata
- Authors
- Nelson, C. R., & Startz, R.
- Venue
- Econometrica
- Publisher
- JSTOR
- Published
- 1990-07
- Volume
- 58
- Issue
- 4
- Pages
- 967
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W1967777997
- Cited by
- 388
- References
- 0
- FWCI
- 2.13
- Type
- journal article
- Language
- en
- Open access
- closed
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