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Bias from Classical and Other Forms of Measurement Error

Posted: 6 Jan 2010  

Guido W. Imbens

Stanford Graduate School of Business

Dean Hyslop

Motu Economic and Public Policy Research

Multiple version iconThere are 2 versions of this paper

Date Written: 2001

Abstract

We consider the implications of a specific alternative to the classical measurement error model, in which the data are optimal predictions based on some information set. One motivation for this model is that if respondents are aware of their ignorance they may interpret the question what is the value of this variable?' as what is your best estimate of this variable?', and provide optimal predictions of the variable of interest given their information set. In contrast to the classical measurement error model, this model implies that the measurement error is uncorrelated with the reported value and, by necessity, correlated with the true value of the variable. In the context of the linear regression framework, we show that measurement error can lead to over- as well as under-estimation of the coefficients of interest. Critical for determining the bias is the model for the individual reporting the mis-measured variables, the individual's information set, and the correlation structure of the errors. We also investigate the implications of instrumental variables methods in the presence of measurement error of the optimal prediction error form and show that such methods may in fact introduce bias. Finally, we present some calculations indicating that the range of estimates of the returns to education consistent with amounts of measurement error found in previous studies. This range can be quite wide, especially if one allows for correlation between the measurement errors.

Keywords: Measurement Error, Predictions, Correlation, Linear regression

JEL Classification: C01, C23

Suggested Citation

Imbens, Guido W. and Hyslop, Dean, Bias from Classical and Other Forms of Measurement Error (2001). Journal of Business and Economic Statistics, Vol. 19, No. 4, pp. 475-481, October 2001. Available at SSRN: https://ssrn.com/abstract=1531968

Guido W. Imbens

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Dean R. Hyslop (Contact Author)

Motu Economic and Public Policy Research ( email )

Level 1, 93 Cuba Street
P.O. Box 24390
Wellington, 6142
New Zealand

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