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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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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
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    6. 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.
    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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