以俄罗斯联邦中央联邦区各地区为例构建综合生产功能并实施

IF 0.6 Q4 BUSINESS
R. Zhukov, N. Kozlova, Evgeny V. Manokhin, M. Plinskaya
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引用次数: 0

摘要

根据一种为估计用于计算动态标准和建立社会经济系统运行绩效综合指标的综合生产函数参数而开发的方法,考虑了一个三维案例。聚合生产函数由子系统元素的功能结果的生产函数及其相关矩阵的二次卷积确定。聚合生产函数的参数是通过求解随机变量的似然函数最大化问题来确定的,似然函数是根据类似规则聚合的生产函数的残差。以克莱纳社会经济系统时空分类框架内的项目子系统为例,我们获得了一个函数的参数调整值,该函数包括F(建筑)、,根据NACE 2的K(金融活动)和固定资产成本(第K节、第F节和第G节总计)、平均年雇员人数(第F节、第G节)和平均年人口(第K部分),基于2015-2020年(第G节、第K节)和2018-2020年(第F部分)中央联邦区各地区的数据。EFRA软件包和Python的项目被用作工具。所获得的结果可供区域当局用于评估各区域的运作情况,并在短期内制定适当的标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Construction of an aggregated production function with implementation based on the example of the regions of the Central Federal District of the Russian Federation
A three-dimensional case is considered on the basis of a method developed for estimating the parameters of the aggregated production function used to calculate dynamic standards and build integral indicators of the performances of the functioning of socio-economic systems. The aggregated production function is determined by the quadratic convolution of the production functions of the results of the functioning of the elements of the subsystem and their correlation matrix. The parameters of the aggregated production function are determined from solving the problem of maximizing the likelihood function of a random variable – the residuals of production functions aggregated according to a similar rule. On the example of a project subsystem within the framework of the Kleiner’s spatial-temporal classification of socio-economic systems we obtained adjusted values of the parameters of a function that includes power-law multiplicative models of the relationship between the volume of gross domestic product by region for sections F (construction), G (wholesale and retail trade), K (financial activity) according to NACE 2 and the cost of fixed assets (total for section K, for sections F and G), the average annual number of employees (for sections F and G) and the average annual population (for section K), based on data for 2015–2020 (sections G, K) and 2018–2020 (section F) for the regions of the Central Federal District. The EFRA software package and Python’s project were used as tools. The results obtained can be used by regional authorities in assessing the functioning of the regions and the formation of appropriate standards in the short term.
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