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A bias-corrected covariance estimator for improved inference when using an unstructured correlation with quadratic inference functions

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  • Westgate, Philip M.

Abstract

Notable bias can exist in the empirical covariance matrix of parameter estimates obtained from the quadratic inference function method that incorporates an unstructured working correlation. We therefore derive a bias correction. Via simulation, we show that the proposed correction leads to appropriate standard error estimation.

Suggested Citation

  • Westgate, Philip M., 2013. "A bias-corrected covariance estimator for improved inference when using an unstructured correlation with quadratic inference functions," Statistics & Probability Letters, Elsevier, vol. 83(6), pages 1553-1558.
    • Handle: RePEc:eee:stapro:v:83:y:2013:i:6:p:1553-1558
    DOI: 10.1016/j.spl.2013.02.021
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    References listed on IDEAS

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    3. Lloyd A. Mancl & Timothy A. DeRouen, 2001. "A Covariance Estimator for GEE with Improved Small‐Sample Properties," Biometrics, The International Biometric Society, vol. 57(1), pages 126-134, March.
    4. Kauermann G. & Carroll R.J., 2001. "A Note on the Efficiency of Sandwich Covariance Matrix Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1387-1396, December.
    5. Han, Peisong & Song, Peter X.-K., 2011. "A note on improving quadratic inference functions using a linear shrinkage approach," Statistics & Probability Letters, Elsevier, vol. 81(3), pages 438-445, March.
    6. Frank Windmeijer, 2000. "A finite sample correction for the variance of linear two-step GMM estimators," IFS Working Papers W00/19, Institute for Fiscal Studies.
    7. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
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