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Asymptotic variance under many instruments: Numerical computations

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  • Abutaliev, Albert
  • Anatolyev, Stanislav

Abstract

In models with many instruments, the asymptotic variance of the LIML estimator contains four components. Apart from the traditional variance, one term is due to instrument numerosity, and the last two appear if the model errors are non-normal. For a stylized instrumental variables model, we compute numerical values of these components to uncover how the four components are related to each other in magnitude.

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Bibliographic Info

Article provided by Elsevier in its journal Economics Letters.

Volume (Year): 118 (2013)
Issue (Month): 2 ()
Pages: 272-274

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Handle: RePEc:eee:ecolet:v:118:y:2013:i:2:p:272-274

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Web page: http://www.elsevier.com/locate/ecolet

Related research

Keywords: Many instruments; Asymptotic variance; LIML; Non-normality;

References

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  1. Christian Hansen & Jerry Hausman & Whitney Newey, 2006. "Estimation with many instrumental variables," CeMMAP working papers CWP19/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. Anatolyev, Stanislav & Gospodinov, Nikolay, 2011. "Specification Testing In Models With Many Instruments," Econometric Theory, Cambridge University Press, vol. 27(02), pages 427-441, April.
  3. Hasselt, Martijn van, 2010. "Many Instruments Asymptotic Approximations Under Nonnormal Error Distributions," Econometric Theory, Cambridge University Press, vol. 26(02), pages 633-645, April.
  4. Anderson, T.W. & Kunitomo, Naoto & Matsushita, Yukitoshi, 2010. "On the asymptotic optimality of the LIML estimator with possibly many instruments," Journal of Econometrics, Elsevier, vol. 157(2), pages 191-204, August.
  5. Jinyong Hahn & Atsushi Inoue, 2002. "A Monte Carlo Comparison Of Various Asymptotic Approximations To The Distribution Of Instrumental Variables Estimators," Econometric Reviews, Taylor & Francis Journals, vol. 21(3), pages 309-336.
  6. Bekker, Paul A, 1994. "Alternative Approximations to the Distributions of Instrumental Variable Estimators," Econometrica, Econometric Society, vol. 62(3), pages 657-81, May.
  7. Hausman & Newey & Woutersen & Chao & Swanson, 2009. "Instrumental Variable Estimation with Heteroskedasticity and Many Instruments," Economics Working Paper Archive 566, The Johns Hopkins University,Department of Economics.
  8. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew "t"-distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389.
  9. Paul A. Bekker & Jan Ploeg, 2005. "Instrumental variable estimation based on grouped data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 59(3), pages 239-267.
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