基于Agent的就业、生产和消费模型An Agent-based Model of Employment, Production and Consumption |
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课程网址: | http://videolectures.net/cvss08_lin_aabme/ |
主讲教师: | Lin Lin |
开课单位: | 基尔大学 |
开课时间: | 2008-10-21 |
课程语种: | 英语 |
中文简介: | 在本文中,将介绍一个基于主体的就业、生产和消费的一般均衡模型,该模型是基于Ian Wright在其论文《资本主义的社会结构》(Physica A 346(2005)589-620)中的设计的扩展工作。游戏规则已经被简化、修改和重组,这导致模拟结果发生了质的变化,也大大增强了模型解释经验分布的能力。该模型的代码是用MatLab和C++编写的,该模型的一个突出性能是,一旦模拟开始,它会迅速收敛到均衡状态,其统计财产与发达资本主义国家的许多经验分布一致,包括对数正态企业规模分布(而非幂律)†,高斯GDP增长分布(代替拉普拉斯)、高斯企业消亡分布(代替对数正态)、衰退指数持续时间分布、高斯工资利润份额分布、对数正态帕累托收入分布等,而本文的目的是引入一种增强的基于agent的模型,该模型不仅将所有这些经验分布统一到一个因果框架中,而且根据经验发现显示出更稳健的模拟结果。 |
课程简介: | In the paper, an agent-based general equilibrium model of employment, production and consumption will be introduced, the model is an extended work based on Ian Wright’s design in his paper "The Social Architecture of Capitalism" (Physica A 346 (2005) 589-620). The game rules have been simplified, modified and restructured, which result in qualitatively changes in simulation outcomes, and it also largely enhanced the model’s capacity in explaining the empirical distributions. The model’s code is written with MatLab and C++, an outstanding performance of the model is that once the simulation starts, it quickly converges to equilibrium with statistical properties that are in agreement with many empirical distributions of developed capitalistic countries, including the lognormal firm size distribution (instead of power law)†, the Gaussian GDP growth distribution (instead of Laplace), the Gaussian firm demise distribution (instead of lognormal), the exponential duration of recession distribution, the Gaussian wage-profit share distribution, the lognormal-pareto income distribution and etc. Normally these distributions are studied in an isolated manner, while the aim of this paper is to introduce an enhanced agent-based model, which not only unifies all these empirical distributions into a causal framework, but also exhibits more robust simulation results in accordance with empirical findings. |
关 键 词: | 均衡模型; 扩展工作; 持续时间 |
课程来源: | 视频讲座网 |
数据采集: | 2023-03-06:chenjy |
最后编审: | 2023-05-11:chenjy |
阅读次数: | 33 |