  It is customary to estimate error-in-variables models using higher-order
moments of observables. This moments-based estimator is consistent only when
the coefficient of the latent regressor is assumed to be non-zero. We develop a
new estimator based on the divide-and-conquer principle that is consistent for
any value of the coefficient of the latent regressor. In an application on the
relation between investment, (mismeasured) Tobin's $q$ and cash flow, we find
time periods in which the effect of Tobin's $q$ is not statistically different
from zero. The implausibly large higher-order moment estimates in these periods
disappear when using the proposed estimator.
