论文检索

利用线性累积量方程对测量误差模型进行最小距离估计AI 解读

We consider a multiple mismeasured regressor errors-in-variables model. We develop closed-form minimum distance estimators from any number of estimating equations, which are linear in the third and higher cumulants of the observable variables. Using the cumulant estimators alters qualitative inference relative to ordinary least squares in two applications related to investment and leverage regressions. The estimators perform well in Monte Carlos calibrated to resemble the…

利用高阶矩的测量误差模型两步 GMM 估计AI 解读

We consider a multiple mismeasured regressor errors-in-variables model where the measurement and equation errors are independent and have moments of every order but otherwise are arbitrarily distributed. We present parsimonious two-step generalized method of moments (GMM) estimators that exploit overidentifying information contained in the high-order moments of residuals obtained by “partialling out” perfectly measured regressors. Using high-order moments requires that the…

测量误差与投资和q之间的关系AI 解读

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…

通过约束误差相关性界定测量误差模型的回归斜率AI 解读

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

未识别测量误差模型中由非正规先验得到正规后验AI 解读

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…