Treating Measurement Error in Tobin'sq
比较了三种测量误差修正方法在投资回归中估计系数的无偏性，发现所有方法在正确设定下表现良好，在错误设定下可能产生偏误，并开发了适用于非平衡面板数据的最小距离技术。
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.