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Instrumental Variables Estimation With Flexible Distributions

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Author Info

  • Hansen, Christian
  • McDonald, James B.
  • Newey, Whitney K.

Abstract

Instrumental variables are often associated with low estimator precision. This paper explores efficiency gains which might be achievable using moment conditions which are nonlinear in the disturbances and are based on flexible parametric families for error distributions. We show that these estimators can achieve the semiparametric efficiency bound when the true error distribution is a member of the parametric family. Monte Carlo simulations demonstrate low efficiency loss in the case of normal error distributions and potentially significant efficiency improvements in the case of thick-tailed and/or skewed error distributions.

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File URL: http://pubs.amstat.org/doi/abs/10.1198/jbes.2009.06161
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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 28 (2010)
Issue (Month): 1 ()
Pages: 13-25

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Handle: RePEc:bes:jnlbes:v:28:i:1:y:2010:p:13-25

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Cited by:
  1. Kerman, Sean C. & McDonald, James B., 2013. "Skewness–kurtosis bounds for the skewed generalized T and related distributions," Statistics & Probability Letters, Elsevier, vol. 83(9), pages 2129-2134.
  2. Tsionas, Efthymios G., 2013. "Bayesian inference in regression with Pearson disturbances," Economics Letters, Elsevier, vol. 118(1), pages 177-181.
  3. Jason Cook & James McDonald, 2013. "Partially Adaptive Estimation of Interval Censored Regression Models," Computational Economics, Society for Computational Economics, vol. 42(1), pages 119-131, June.

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