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Nonparametric Bounds on the Income Distribution in the Presence of Item Nonresponse

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Author Info

  • Vazquez-Alvarez, R.
  • Melenberg, B.
  • Soest, A.H.O. van

    (Tilburg University, Center for Economic Research)

Abstract

Item nonresponse in micro surveys can lead to biased estimates of the parameters of interest if such nonresponse is nonrandom. Selection models can be used to correct for this, but parametric and semiparametric selection models require additional assumptions. Manski has recently developed a new approach, showing that, without additional assumptions, the parameters of interest are identified up to some bounding interval. In this paper, we apply Manski’s approach to estimate the distribution function and quantiles of personal income, conditional on given covariates, taking account of item nonresponse on income. Nonparametric techniques are used to estimate the bounding intervals. We consider worst case bounds, as well as bounds which are valid under nonparametric assumptions on monotonicity or under exclusion restrictions.

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Bibliographic Info

Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 1999-33.

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Date of creation: 1999
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Handle: RePEc:dgr:kubcen:199933

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Related research

Keywords: nonparametrics; bounds and identification; sample non-response;

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References

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  1. repec:att:wimass:9604 is not listed on IDEAS
  2. repec:att:wimass:9001 is not listed on IDEAS
  3. Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July.
  4. HÄRDLE, Wolfgang, 1992. "Applied nonparametric methods," CORE Discussion Papers 1992003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, vol. 80(2), pages 313-18, May.
  6. repec:att:wimass:9217 is not listed on IDEAS
  7. Newey, Whitney K & Powell, James L & Walker, James R, 1990. "Semiparametric Estimation of Selection Models: Some Empirical Results," American Economic Review, American Economic Association, vol. 80(2), pages 324-28, May.
  8. Oliver LINTON, . "Applied nonparametric methods," Statistic und Oekonometrie 9312, Humboldt Universitaet Berlin.
  9. Manski, C.F., 1990. "The Selection Problem," Working papers 90-12, Wisconsin Madison - Social Systems.
  10. Charles F. Manski, 1997. "Monotone Treatment Response," Econometrica, Econometric Society, vol. 65(6), pages 1311-1334, November.
  11. Heckman, James J, 1979. "Sample Selection Bias as a Specification Error," Econometrica, Econometric Society, vol. 47(1), pages 153-61, January.
  12. Härdle, Wolfgang, 1984. "Robust regression function estimation," Journal of Multivariate Analysis, Elsevier, vol. 14(2), pages 169-180, April.
  13. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  14. Chaudhuri, Probal, 1991. "Global nonparametric estimation of conditional quantile functions and their derivatives," Journal of Multivariate Analysis, Elsevier, vol. 39(2), pages 246-269, November.
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Cited by:
  1. Vazquez-Alvarez, R., 2001. "A Nonparametric Approach to the Sample Selection Problem in Survey Data," Open Access publications from Tilburg University urn:nbn:nl:ui:12-86303, Tilburg University.

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