Uniform inference in linear error-in-variables models: Divide-and-conquer
针对线性测量误差模型，传统高阶矩估计仅在潜变量系数非零时一致，本文提出基于分治原则的新估计量，在任何系数值下均一致，并应用于投资与托宾q关系研究，发现某些时期托宾q效应不显著。
.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 nonzero. 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.