Panel Regression with Random Noise
AbstractThe 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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Bibliographic InfoPaper provided by CESifo Group Munich in its series CESifo Working Paper Series with number 2608.
Date of creation: 2009
Date of revision:
panel regression; multiplicative measurement errors; bias correction; asymptotic variance; disclosure control;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
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- Lin, An-loh, 1989. "Estimation of multiplicative measurement-error models and some simulation results," Economics Letters, Elsevier, vol. 31(1), pages 13-20.
- Wansbeek, Tom, 2001. "GMM estimation in panel data models with measurement error," Journal of Econometrics, Elsevier, vol. 104(2), pages 259-268, September.
- Zvi Griliches & Jerry A. Hausman, 1984.
"Errors in Variables in Panel Data,"
NBER Technical Working Papers
0037, National Bureau of Economic Research, Inc.
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