Multiplicative Errors-in-Variables Models with Applications to Recent Data Released by the U.S. Department of Energy

Abstract In an errors-in-variables model, the predicting variables are observed with errors. Traditionally, the errors are assumed to be additive. In this article, I consider the case in which the error is multiplicative, a situation that arises when analyzing some recent data released by the U.S. Department of Energy. A consistent estimator is provided for regression coefficients β by correcting the asymptotic bias of the least squares estimate. It is shown that , after being normalized by an estimate of its covariance, is asymptotically normally distributed. This can, therefore, be used to construct approximate tests and confidence sets for β. The results are essentially nonparametric.