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The Consequences of Non-Classical Measurement Error for Distributional Analysis

Author

Listed:
  • D. O'Neill

    (Department of Economics, Maynooth, Ireland)

  • Sweetman. O.
  • Van de gaer D.

Abstract

This paper analyzes the consequences of non-classical measurement error for distributional analysis. We show that for a popular set of distributions negative correlation between the measurement error (u) and the true value (y) may reduce the bias in the estimated distribution at every value of y. For other distributions the impact of non-classical measurement di¤ers throughout the support of the distribution. We illustrate the practical importance of these results using models of unemployment duration and income.

Suggested Citation

  • D. O'Neill & Sweetman. O. & Van de gaer D., 2005. "The Consequences of Non-Classical Measurement Error for Distributional Analysis," Economics Department Working Paper Series n1490205, Department of Economics, National University of Ireland - Maynooth.
    • Handle: RePEc:may:mayecw:n1490205
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    File URL: http://repec.maynoothuniversity.ie/mayecw-files/N1490205.pdf
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    References listed on IDEAS

    as
    1. Kiefer, Nicholas M, 1988. "Economic Duration Data and Hazard Functions," Journal of Economic Literature, American Economic Association, vol. 26(2), pages 646-679, June.
    2. Andrew Chesher & Christian Schluter, 2002. "Welfare Measurement and Measurement Error," Review of Economic Studies, Oxford University Press, vol. 69(2), pages 357-378.
    3. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    4. Pieter Serneels, 2002. "Explaining Non-Negative Duration Dependence Among the Unemployed," CSAE Working Paper Series 2002-13, Centre for the Study of African Economies, University of Oxford.
    5. Horowitz, Joel L & Manski, Charles F, 1995. "Identification and Robustness with Contaminated and Corrupted Data," Econometrica, Econometric Society, vol. 63(2), pages 281-302, March.
    6. Torelli, Nicola & Trivellato, Ugo, 1989. "Youth unemployment duration from the Italian labour force survey: Accuracy issues and modelling attempts," European Economic Review, Elsevier, vol. 33(2-3), pages 407-415, March.
    7. Zimmerman, David J, 1992. "Regression toward Mediocrity in Economic Stature," American Economic Review, American Economic Association, vol. 82(3), pages 409-429, June.
    8. Bound, John & Brown, Charles & Duncan, Greg J & Rodgers, Willard L, 1994. "Evidence on the Validity of Cross-Sectional and Longitudinal Labor Market Data," Journal of Labor Economics, University of Chicago Press, vol. 12(3), pages 345-368, July.
    9. Chesher, Andrew & Dumangane, Montezuma & Smith, Richard J., 2002. "Duration response measurement error," Journal of Econometrics, Elsevier, vol. 111(2), pages 169-194, December.
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    Cited by:

    1. Donal O’Neill & Olive Sweetman & Dirk Van de gaer, 2007. "The effects of measurement error and omitted variables when using transition matrices to measure intergenerational mobility," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 5(2), pages 159-178, August.

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    Keywords

    Distribution functions; Non-classical measurement error;

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