  This paper analyzes the classical linear regression model with measurement
errors in all the variables. First, we provide necessary and sufficient
conditions for identification of the coefficients. We show that the
coefficients are not identified if and only if an independent normally
distributed linear combination of regressors can be transferred from the
regressors to the errors. Second, we introduce a new estimator for the
coefficients using a continuum of moments that are based on second derivatives
of the log characteristic function of the observables. In Monte Carlo
simulations, the estimator performs well and is robust to the amount of
measurement error and number of mismeasured regressors. In an application to
firm investment decisions, the estimates are similar to those produced by a
generalized method of moments estimator based on third to fifth moments.
