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, and potentially several others.

翻译：本文提出了一种针对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何目标函数驱动，而是对观测对斜率进行简单加权平均的结果。研究表明，通过精心选择权重，该估计量具有一致性。为促进有效的统计推断，本文还推导了其渐近分布等其他性质。与最小二乘法和最大似然法等传统方法不同，该估计量的残差估计值并非通过构造与模型解释变量正交。这一特性使得该方法在众多实际应用中具有广泛适用性，例如可检验内生性（即解释变量与扰动项之间的相关性），并可能拓展至其他领域。