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On skewness and kurtosis of econometric estimators

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  • Yong Bao
  • Aman Ullah

Abstract

We derive the approximate results for two standardized measures of deviation from normality, namely, the skewness and excess kurtosis coefficients, for a class of econometric estimators. The results are built on a stochastic expansion of the moment condition used to identify the econometric estimator. The approximate results can be used not only to study the finite sample behaviour of a particular estimator, but also to compare the finite sample properties of two asymptotically equivalent estimators. We apply the approximate results to the spatial autoregressive model and find that our results approximate the non-normal behaviours of the maximum likelihood estimator reasonably well. However, when the weights matrix becomes denser, the finite sample distribution of the maximum likelihood estimator departs more severely from normality and our results provide less accurate approximation. Copyright © 2009 The Author(s). Journal compilation © Royal Economic Society 2009

Suggested Citation

  • Yong Bao & Aman Ullah, 2009. "On skewness and kurtosis of econometric estimators," Econometrics Journal, Royal Economic Society, vol. 12(2), pages 232-247, July.
    • Handle: RePEc:ect:emjrnl:v:12:y:2009:i:2:p:232-247
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    Cited by:

    1. Wang, Xiaohu & Phillips, Peter C.B. & Yu, Jun, 2011. "Bias in estimating multivariate and univariate diffusions," Journal of Econometrics, Elsevier, vol. 161(2), pages 228-245, April.
    2. Yong Bao & Aman Ullah, 2021. "Analytical Finite Sample Econometrics: From A. L. Nagar to Now," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 17-37, December.

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