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Bias Corrections In Testing And Estimating Semiparametric, Single Index Models

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  • Klein, Roger
  • Shen, Chan

Abstract

Semiparametric methods are widely employed in applied work where the ability to conduct inferences is important. To establish asymptotic normality for making inferences, bias control mechanisms are often used in implementing semiparametric estimators. The first contribution of this paper is to propose a mechanism that enables us to establish asymptotic normality with regular kernels. In so doing, we argue that the resulting estimator performs very well in finite samples.

Suggested Citation

  • Klein, Roger & Shen, Chan, 2010. "Bias Corrections In Testing And Estimating Semiparametric, Single Index Models," Econometric Theory, Cambridge University Press, vol. 26(06), pages 1683-1718, December.
  • Handle: RePEc:cup:etheor:v:26:y:2010:i:06:p:1683-1718_99
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    Cited by:

    1. Klein, Roger & Shen, Chan & Vella, Francis, 2015. "Estimation of marginal effects in semiparametric selection models with binary outcomes," Journal of Econometrics, Elsevier, vol. 185(1), pages 82-94.
    2. Chan Shen & Roger Klein, 2017. "Recursive Differencing: Bias Reduction with Regular Kernels," Departmental Working Papers 201701, Rutgers University, Department of Economics.
    3. Biavaschi, Costanza, 2016. "Recovering the counterfactual wage distribution with selective return migration," Labour Economics, Elsevier, vol. 38(C), pages 59-80.
    4. Roger Klein & Chan Shen & Francis Vella, 2014. "Semiiparametric Selection Models with Binary Outcomes," Departmental Working Papers 201403, Rutgers University, Department of Economics.
    5. Lee, Jiyon, 2015. "A semiparametric single index model with heterogeneous impacts on an unobserved variable," Journal of Econometrics, Elsevier, vol. 184(1), pages 13-36.
    6. Roger Klein & Chan Shen & Francis Vella, 2011. "Semiparametric selection models with binary outcomes," CeMMAP working papers CWP30/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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