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Bias-Corrected Moment-Based Estimators for Parametric Models Under Endogenous Stratified Sampling

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  • Esmeralda Ramalho
  • Joaquim Ramalho

Abstract

This paper provides an integrated approach for estimating parametric models from endogenous stratified samples. We discuss several alternative ways of removing the bias of the moment indicators usually employed under random sampling for estimating the parameters of the structural model and the proportion of the strata in the population. Those alternatives give rise to a number of moment-based estimators that are appropriate for both cases where the marginal strata probabilities are known and unknown. The derivation of our estimators is very simple and intuitive and incorporates as particular cases most of the likelihood-based estimators previously suggested by other authors.

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File URL: http://www.tandfonline.com/doi/abs/10.1080/07474930600972574
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 25 (2006)
Issue (Month): 4 ()
Pages: 475-496

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Handle: RePEc:taf:emetrv:v:25:y:2006:i:4:p:475-496

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Related research

Keywords: Bias correction; Endogenous stratified sampling; GMM; Parametric models;

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Cited by:
  1. Ramalho Esmeralda A., 2010. "Covariate Measurement Error: Bias Reduction under Response-Based Sampling," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-34, September.
  2. Prokhorov, Artem & Schmidt, Peter, 2009. "GMM redundancy results for general missing data problems," Journal of Econometrics, Elsevier, vol. 151(1), pages 47-55, July.

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