Modelling Rule- and Experience-Based Expectations Using Neuro-Fuzzy-Systems
Modelling Rule- and Experience-Based Expectations Using Neuro-Fuzzy-Systems
复制标题
使用神经模糊系统对基于规则和经验的期望进行建模
DOI：
--
复制
发表时间：
1999
期刊：
影响因子：
--
通讯作者：
Stefan Kooths
中科院分区：
文献类型：
--
作者：
Stefan Kooths
宏观经济理论中的预期模型往往是在关于人们学习能力和知识水平的限制性假设下进行的。要么假设人们根本不学习，这证明使用简单的自回归预测方法是合理的，要么模型制造者认为相关的代理人知道经济系统（长期）行为的一切（理性预期）。这两种方法似乎都不能真实地描述人们在做出当前决策时，在预测未来发展时所做的事情。在商业周期理论中，预期在主要宏观经济指标的周期性行为中起着主导作用，缺乏适当的预期模型尤其成问题。本文提供了一个更现实的人类预测行为的描述，通过使用神经模糊系统在模拟环境中的经济预期模型。模糊规则允许表达模糊的知识，例如，“如果货币供应量相当高，失业率相当低，那么通货膨胀往往会大幅上升。“这种方法假设人们对经济依赖性有所了解，但他们不知道确切的公式。当人们用“相当高”或“非常低”来限定货币供应量的某个增长率时，神经方法能够训练他们的意思。这两种技术被混合为一个神经模糊系统，称为“神经模糊期望生成器（NFEG）”。“该模块连接到使用MAKROMAT-nfx（为WinNT 4.0和Win98设计）的商业周期模拟模型。该软件使我们能够分析NFEG如何与经济系统相互作用时，后者暴露于外生冲击。由于传统形式的预期建模也在软件中实现，因此也可以在基于规则和经验的预期与自回归或理性预期之间进行有趣的比较。
Expectations modelling in macroeconomic theory is often done under restrictive assumptions regarding people's ability to learn and the level of their knowledge. Either it is assumed that people do not learn at all, which justifies the use of simple autoregressive forecasting methods, or the model makers believe that the relevant agents know everything about the (long-term) behaviour of the economic system (rational expectations). Neither of these seems realistically to describe what people really do in anticipating future developments when making current decisions. The lack of an adequate expectations model is especially problematic in business cycle theory where expectations play a dominant role in the cyclical behaviour of main macroeconomic indicators. This paper provides a more realistic description of human forecasting behaviour by using neuro-fuzzy-systems to model economic expectations in a simulation environment. Fuzzy-rules allow the expression of vague knowledge, e.g. "IF the money supply is fairly high and the unemployment rate is rather low THEN inflation tends to rise considerably." This approach, then, assumes that people know something about economic dependencies but that they are not informed of the exact formulas. Neuro-methods are able to train on what people mean when they qualify a certain growth rate of money supply in terms such as "fairly high" or "very low." These two techniques are hybridized as a neuro-fuzzy-system called the "Neuro-Fuzzy Expectation Generator (NFEG)." This module is connected to a business cycle simulation model using MAKROMAT-nfx (designed for WinNT 4.0 and Win98). This software allows us to analyze how the NFEG interacts with the economic system when the later is exposed to exogenous shocks. Since traditional forms of expectations modelling are also implemented in the software, interesting comparisons between rule- and experience-based expectations and autoregressive or rational expectations are possible as well.