REMEDIES FOR THE ERRORS-IN-VARIABLES problem often take the form of consistently estimable bounds on a parameter, the advantage of such remedies being that they require weaker assumptions than those needed for consistent estimation of the parameter itself. The seminal result is the "errors-in-variables bound " of Gini (1921), which

The problem considered is inference in a simple errors-in-variables model where consistent estimation is impossible without introducing additional exact prior information. The probabilistic prior information required for Bayesian analysis is found to be surprisingly light: despite the model's lack of identification a proper posterior is guaranteed for any bounded prior density, including those representing improper priors. This result is illustrated with the improper uniform prior, which implies marginal posterior densities obtainable by integrating the likelihood function; surprisingly, the posterior mode for the regression slope is the usual least squares estimate. KEYwoRDs: Errors-in-variables, Bayesian inference, identification, improper priors, proper posteriors, finitely additive probabilities, coherence. 1.

We compare the ability of three measurement error remedies to deliver unbiased estimates of coefficients in investment regressions. We examine high-order moment estimators, dynamic panel estimators, and simple instrumental variables estimators that use lagged mismeasured regressors as instruments. We show that recent investigations of this question are largely uninformative. We find that all estimators can perform well under correct specification, all can be biased under misspecification, and misspecification is easiest to detect in the case of high-order moment estimators. We develop and demonstrate a minimum distance technique that extends the high-order moment estimators to be used on unbalanced panel data. Published by Oxford University Press 2011., Oxford University Press.

Many recent empirical investment studies have found that the investment of financially constrained firms responds strongly to cash flow. Paralleling these findings is the disappointing performance of the q theory of investment: even though marginal q should summarize the effects of all factors relevant to the investment decision, cash flow still matters. We examine whether this failure is due to error in measuring marginal q. Using measurement errorconsistent generalized method of moments estimators, we find that most of the stylized facts produced by investment-q cash flow regressions are artifacts of measurement error. Cash flow does not matter, even for financially constrained firms, and despite its simple structure, q theory has good explanatory power once purged of measurement error.