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More efficient estimation under non-normality when higher moments do not depend on the regressors, using residual augmented least squares

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  • Im, Kyung So
  • Schmidt, Peter

Abstract

Under normality, least squares is efficient. However, if the errors are not normal, we can gain efficiency from the assertion that higher moments do not depend on the regressors. In this paper, we show how the assumption that higher moments do not depend on the regressors can be exploited in a GMM framework, and we provide simple estimators that are asymptotically equivalent to the GMM estimators. These estimators can be calculated by linear regressions which have been augmented with functions of the least squares residuals.

Suggested Citation

  • Im, Kyung So & Schmidt, Peter, 2008. "More efficient estimation under non-normality when higher moments do not depend on the regressors, using residual augmented least squares," Journal of Econometrics, Elsevier, vol. 144(1), pages 219-233, May.
    • Handle: RePEc:eee:econom:v:144:y:2008:i:1:p:219-233
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    References listed on IDEAS

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    1. Thomas E. MaCurdy, 1982. "Using Information on the Moments of Disturbances to Increase the Efficiency of Estimation," NBER Technical Working Papers 0022, National Bureau of Economic Research, Inc.
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    7. Qian, Hailong & Schmidt, Peter, 1999. "Improved instrumental variables and generalized method of moments estimators," Journal of Econometrics, Elsevier, vol. 91(1), pages 145-169, July.
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