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Panel Regression with Random Noise

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  • Gerd Ronning
  • Hans Schneeweiss
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    Abstract

    The paper explores the effect of measurement errors on the estimation of a linear panel data model. The conventional fixed effects estimator, which ignores measurement errors, is biased. By correcting for the bias one can construct consistent and asymptotically normal estimators. In addition, we find estimates for the asymptotic variances of these estimators. The paper focuses on multiplicative errors, which are often deliberately added to the data in order to minimize their disclosure risk. They can be analyzed in a similar way as additive errors, but with some important and consequential differences.

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    File URL: http://www.cesifo-group.de/portal/page/portal/DocBase_Content/WP/WP-CESifo_Working_Papers/wp-cesifo-2009/wp-cesifo-2009-04/cesifo1_wp2608.pdf
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    Bibliographic Info

    Paper provided by CESifo Group Munich in its series CESifo Working Paper Series with number 2608.

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    Date of creation: 2009
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    Handle: RePEc:ces:ceswps:_2608

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    Related research

    Keywords: panel regression; multiplicative measurement errors; bias correction; asymptotic variance; disclosure control;

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    References

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    1. Zvi Griliches & Jerry A. Hausman, 1984. "Errors in Variables in Panel Data," NBER Technical Working Papers, National Bureau of Economic Research, Inc 0037, National Bureau of Economic Research, Inc.
    2. Lin, An-loh, 1989. "Estimation of multiplicative measurement-error models and some simulation results," Economics Letters, Elsevier, Elsevier, vol. 31(1), pages 13-20.
    3. Wansbeek, Tom, 2001. "GMM estimation in panel data models with measurement error," Journal of Econometrics, Elsevier, Elsevier, vol. 104(2), pages 259-268, September.
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