Econometrica Vol. 48 No. 6 1980
Extending the Classical Normal Errors-in-Variables Model
Abstract
IT IS WELL KNOWN that least-squares estimates of the coefficients of a regression equation are inconsistent if any of the regressors are measured with error. The nature of these inconsistencies has been examined by Aigner [1], Blomqvist [2], Chow [3], Levi [5], McCallum [6], and Wickens [10] for the case in which a single regressor is subject to measurement error. The purpose of this study is to examine the nature of these inconsistencies when more than one variable is measured with error. We begin by reviewing the case of one variable measured with error, developing a unified treatment of issues which previously have been discussed separately. Concentrating on the case in which two regressors are measured with error, we then examine how the predictions of the one erroneously measured regressor model must be qualified when more than one regressor is subject to measurement error.
- DOI
- 10.2307/1912823
- Volume
- 48
- Issue
- 6
- Pages
- 1541
- Sources
- bibtex:phds-export.bib crossref openalex