Bias From Classical and Other Forms of Measurement Error
探讨一种替代经典测量误差模型的方案，其中观测到的错误数据是基于某些信息集对真实值的最优预测。在这种模型下，解释变量中的测量误差不会导致偏误，而被解释变量中的测量误差会导致向零的偏误。
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.