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Bias from Classical and Other Forms of Measurement Error

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  • Hyslop, Dean R
  • Imbens, Guido W

Abstract

We consider the implications of an alternative to the classical measurement-error model, in which the observed, mismeasured data are optimal predictions of the true values, given some information set. In this model, any measurement error is uncorrelated with the reported value and, by necessity, correlated with the true value of interest. In a regression model, such measurement error in the regressor does not lead to bias, whereas measurement error in the dependent variable leads to bias toward 0. In general, the measurement-error model, together with the information set, is critical for determining the bias in econometric estimates.

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Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 19 (2001)
Issue (Month): 4 (October)
Pages: 475-81

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Handle: RePEc:bes:jnlbes:v:19:y:2001:i:4:p:475-81

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  1. David Card & Dean Hyslop, 1997. "Does Inflation “Grease the Wheels of the Labor Market”?," NBER Chapters, in: Reducing Inflation: Motivation and Strategy, pages 71-122 National Bureau of Economic Research, Inc.
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  8. repec:att:wimass:8905 is not listed on IDEAS
  9. 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.
  10. Pischke, J.S., 1994. "Measurement Error and Earnings Dynamics: Some Estimates from the PSID Validation Study," Working papers 94-01, Massachusetts Institute of Technology (MIT), Department of Economics.
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