The paper introduces a new estimation method for the standard linear regression model. The procedure is not driven by the optimisation of any objective function rather, it is a simple weighted average of slopes from observation pairs. The paper shows that such estimator is consistent for carefully selected weights. Other properties, such as asymptotic distributions, have also been derived to facilitate valid statistical inference. Unlike traditional methods, such as Least Squares and Maximum Likelihood, among others, the estimated residual of this estimator is not by construction orthogonal to the explanatory variables of the model. This property allows a wide range of practical applications, such as the testing of endogeneity, i.e., the correlation between the explanatory variables and the disturbance terms.

翻译：本文提出了一种适用于标准线性回归模型的新型估计方法。该过程并非通过优化任意目标函数驱动，而是对观测对斜率的简单加权平均。研究表明，在精心选取权重的情况下，该估计量具有一致性。为便于进行有效的统计推断，本文还推导了其渐近分布等其他性质。与最小二乘法、极大似然法等传统方法不同，该估计量的残差在构造上不与模型解释变量正交。这一特性使其具有广泛的实际应用价值，例如可用于内生性检验——即解释变量与扰动项之间的相关性检验。