Averaged Singular Integral Estimation as a Bias Reduction Technique
AbstractThis paper proposes an averaged version of singular integral estimators, whose bias achieves higher rates of convergence under smoothing assumptions. We derive exact bias bounds, without imposing smoothing assumptions, which are a basis for deriving the rates of convergence under differentiability assumptions.
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Bibliographic InfoPaper provided by Universidad Carlos III de Madrid in its series Open Access publications from Universidad Carlos III de Madrid with number info:hdl:10016/2453.
Length: 139 p.
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global rates of convergence for the bias; singular integral estimators; generalized jackknife; bias reduction techniques;
Other versions of this item:
- Delgado, Miguel A. & Vidal-Sanz, Jose M., 2002. "Averaged Singular Integral Estimation as a Bias Reduction Technique," Journal of Multivariate Analysis, Elsevier, vol. 80(1), pages 127-137, January.
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- Robinson, P M, 1989. "Hypothesis Testing in Semiparametric and Nonparametric Models for Econometric Time Series," Review of Economic Studies, Wiley Blackwell, vol. 56(4), pages 511-34, October.
- Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, vol. 57(6), pages 1403-30, November.
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