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The Efficiency of OLS Estimators of Structural Parameters in a Simple Linear Regression Model in the Calibration of the Averages Scheme

机译:简单线性回归模型中结构参数的OLS估计器在均值方案校准中的效率

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A simple linear regression model is one of the pillars of classic econometrics. Multiple areas of research function within its scope. One of the many fundamental questions in the model concerns proving the efficiency of the most commonly used OLS estimators and examining their properties. In the literature of the subject one can find taking back to this scope and certain solutions in that regard. Methodically, they are borrowed from the multiple regression model or also from a boundary partial model. Not everything, however, is here complete and consistent. In the paper a completely new scheme is proposed, based on the implementation of the Cauchy-Schwarz inequality in the arrangement of the constraint aggregated from calibrated appropriately secondary constraints of unbiasedness which in a result of choice the appropriate calibrator for each variable directly leads to showing this property. A separate range-is a matter of choice of such a calibrator. These deliberations, on account of the volume and kinds of the calibration, were divided into a few parts. In the one the efficiency of OLS estimators is proven in a mixed scheme of the calibration by averages, that is preliminary, and in the most basic frames of the proposed methodology. In these frames the future outlines and general premises constituting the base of more distant generalizations are being created.
机译:一个简单的线性回归模型是经典计量经济学的支柱之一。研究的多个领域在其范围内起作用。模型中的许多基本问题之一涉及证明最常用的OLS估计器的效率并检查其属性。在该主题的文献中,人们可以找到这一方面的范围和某些解决方案。从方法上讲,它们是从多元回归模型或边界局部模型中借用的。但是,并非所有内容都完整且一致。在本文中,基于Cauchy-Schwarz不等式的实施方案,提出了一种全新的方案,该方案由已校准的,适当的无偏次要约束聚合而成的约束布置而产生,在选择结果中,每个变量的合适校准器直接导致显示这个性质。单独的量程是此类校准器的选择问题。考虑到校准的数量和种类,这些审议分为几部分。在一种方法中,OLS估计器的效率在平均值的混合校准方案中得到了证明,该方案是初步的,并且是所提出方法的最基本框架。在这些框架中,正在创建构成更远概括的基础的未来轮廓和一般前提。

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