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经济模型之变

来源:数学 时间:2018-12-15 点击:

MAINSTREAM economics has always had its dissidents. But the discipline’s failure to predict the financial crisis has made the ground especially fertile for a rethink.

主流经济学向来不缺反对的声音,这次它未能预测到金融危机使得人们更有理由对其进行反思。

Critics tend to agree on what is wrong with current macroeconomic forecasting. A hearing of the House of Representatives Committee of Science and Technology on July 20th targeted the “dynamic stochastic general equilibrium” (DSGE) models used by the Federal Reserve and other central banks. The hearing aimed to “question the wisdom of relying for national economic policy on a single, specific model when alternatives are available.” The Institute for New Economic Thinking in New York, which had its inaugural conference in April, has attacked many of the assumptions, including efficient financial markets and rational expectations, on which these models are predicated. These assumptions were clearly too simplistic. But there is less agreement on what should replace the old ways.

批评者对当前宏观经济预测错在何处基本达成共识。7月20日,美国众议院科学与技术委员会的听证会把矛头对准了美联储和其他中央银行所实行的“动态随机一般均衡”(DSGE)模型,“质疑国家经济政策抛开其他可选模型,而仅依赖单一的、特定的经济模型是否明智”。四月份成立于纽约的“新经济思维研究所”在其创立大会上抨击包括高效金融市场和理性预期在内的诸多经济学假设,这些假设为经济模型对经济的预测提供了基础,但显然过于简单化。然而,用什么取代这些陈旧的假设,大家却是莫衷一是。

One of the most promising options was the topic of a workshop in Virginia at the end of June. The workshop was funded by America’s National Science Foundation and attended by a diverse bunch that included economists from the Fed and the Bank of England, policy advisers and computer scientists. They were there to explore the potential of “agent-based models” (ABMs) of the economy to help learn the lessons of this crisis and, perhaps, to develop an early-warning system for the next one.

六月底在弗吉尼亚州召开的一个专题研讨会上讨论的议题最有希望。这次研讨活动由美国国家科学基金会出资举办,包括联邦储备局和英格兰银行的经济学家、政策顾问和电脑科学家等多领域人士参加,探讨了经济上采用“基于个体模型”(ABMs)来帮助汲取此次金融危机教训、开发早期预警系统以防再次发生金融危机的可能性。

Agent-based modelling does not assume that the economy can achieve a settled equilibrium. No order or design is imposed on the economy from the top down. Unlike many models, ABMs are not populated with “representative agents”: identical traders, firms or households whose individual behaviour mirrors the economy as a whole. Rather, an ABM uses a bottom-up approach which assigns particular behavioural rules to each agent. For example, some may believe that prices reflect fundamentals whereas others may rely on empirical observations of past price trends.

基于个体模型认为经济无法实现稳定的均衡,也不会有自上而下强加的秩序或者顶层设计。和许多模型不同,ABMs不要求有很多的代表性个体参与。代表性个体是指同类的经营者、公司或商家的代表,其个体行为映射出整体经济的全貌。相反,这一模型是自下而上的,每一个体都有其独特的行为准则。比如,有的个体会认为价格反映基本面,而另一些则通过身体力行观察以往的价格走势定价。

Crucially, agents’ behaviour may be determined (and altered) by direct interactions between them, whereas in conventional models interaction happens only indirectly through pricing. This feature of ABMs enables, for example, the copycat behaviour that leads to “herding” among investors. The agents may learn from experience or switch their strategies according to majority opinion. They can aggregate into institutional structures such as banks and firms. These things are very hard, sometimes impossible, to build into conventional models. But in an agent-based model you simply run a computer simulation to see what emerges, free from any top-down assumptions.

关键是,个体行为取决于个体间直接的互动,因势而变,而在传统的模型中,个体间的互动则仅限于定价这一环节,是间接的。ABMs模型的这一特点会造成,比如说,投资扎堆,盲目模仿的“羊群”效应。这些个体会凭经验或者按照大多数人的观点改变策略。他们会形成象银行和公司那样的制度性结构。这一切在传统模型中是很难或不可能发生的。但是,在基于个体模型中,你只需进行电脑模拟就会看到下面要发生什么,完全不用自上而下的假设。

Although DSGE models are also based on microeconomic foundations, they accept the traditional view that there exists some ideal equilibrium towards which all prices are drawn. That this is often approximately true is why DSGE models perform well enough in a business-as-usual economy. They do badly in a crisis, however, because their “dynamic stochastic” element only amounts to minor fluctuations around a state of equilibrium, and there is no equilibrium during crashes.

尽管“动态随机一般均衡”(DSGE)模型也是基于微观经济而建,但它秉承传统的观点,认为所有价格形成都有理想的均衡状态,事实也大致如此,因此,DSGE模型在经济运行正常时表现得很完美。然而,一遇到经济危机,它就难以为继了,因为其“动态随机”要素仅能说明均衡状态附近的微小价格波动,在危机来临时却无均衡状态可言。

ABMs, in contrast, make no assumptions about the existence of efficient markets or general equilibrium. The markets that they generate are more like a turbulent river or the weather system, subject to constant storms and seizures of all sizes. Big fluctuations and even crashes are an inherent feature. That is because ABMs contain feedback mechanisms that can amplify small effects, such as the herding and panic that generate bubbles and crashes. In mathematical terms the models are “non-linear”, meaning that effects need not be proportional to their causes.

ABMs并不假设存在有效的市场调节机制或者一般均衡,相反,这一模型认为市场更像一条汹涌澎湃的河流或者剧烈变化的天气系统,大幅波动、甚至崩盘是其固有的特点。正因为如此,ABMs模型中设计了反馈机制,能够把引起泡沫或崩盘的“羊群效应”或者市场恐慌等事件放大观察。用数学术语讲,这一模型是“非线性的”,表示“因与果之间是不成比例的”。

These non-linearities were clearly on show in the credit crunch. At the workshop Andrew Lo of the Massachusetts Institute of Technology presented a model of the American housing market, inspired by ABM approaches, which showed how a fateful conjunction of rising house prices, falling interest rates and easy access to refinancing created an awesome burden of debt. John Geanakoplos of Yale University explained how the debt cycle in remortgaging—high amounts of leverage during booms, low amounts during recessions—can act like an out-of-control pendulum to create instability. Sujit Kapadia of the Bank of England is trying to model the web of interdependencies created by the use of complex derivatives. These “network-based vulnerabilities” are just the kind of thing that ABMs are good at capturing. 

非线性特征在信用危机中表现明显。专题研讨会上麻省理工学院学院的安德鲁•罗给大家展示了一个美国房地产市场的模型,该模型受ABM模型启发,向人们展示了房价上升、利率下降、更易于借贷三者要命的关联是如何引发可怕的债务危机的。耶鲁大学的约翰•珍娜卡普拉斯解释了次贷中的债务链是如何象失去控制的钟摆一样引发不稳定的 —— 经济繁荣时发挥着强大的杠杆作用,经济衰退时却急剧萎缩。英格兰银行的Sujit Kapadia设计一个模型来模拟由复杂的金融衍生品构成的相互依存的网络,而捕捉这些“网状体系的脆弱性”正是ABMs模型的强项。

Model behaviour 模型的运作
Another big lesson of the crisis is the role of interactions between different sectors of the economy—housing and finance, say. Although conventional models can incorporate these, ABMs may be better tailored to modelling specific sectors. The organisers of the Virginia workshop—Doyne Farmer of the Santa Fe Institute and Robert Axtell of George Mason University—wanted to explore the feasibility of constructing an immense ABM of the entire global economy by “wiring” many such modules together.

这次金融危机的另一大教训是,不同经济领域——比如房地产和金融——之间相互影响会造成严重的后果。传统经济模型也会统筹考虑这些不同的领域,但ABMs模型能够更容易地做出调整以反映具体的经济领域。这次弗吉尼亚专题研讨会的组织者,圣达菲学院的Doyne Farmer和乔治梅森大学的Robert Axtell,想要探索通过把多个具体的ABM模型“串”起来,构建覆盖全球经济的庞大的ABM模型的可行性。

What might be required for such an enterprise? One vision is a real-time simulation, fed by masses of data, that would operate rather like the traffic-forecasting models now used in Dallas and in the North Rhine-Westphalia region of Germany. But it might be more realistic and useful to employ a suite of such models, in the manner of global climate simulations, which project various possible futures. In either case, the models would need much more data on the activities of individuals, banks and companies.

如此宏大的计划可能会需要什么呢?一种是需要经济运行的实时模拟,由庞大的数据库支持,运行很像目前在达拉斯市和德国北莱茵-威斯特法利亚地区所使用的交通预报模型。但一种像全球气候模拟系统那样,能够预测未来经济运行的各种可能性的模型可能会更加实际、有用。不管怎样,这两种模型都需要个人、银行和公司活动的大量数据。更多信息请访问:http://www.24en.com/

Such data-gathering raises privacy fears but is essential. Seismologists may not be able to forecast earthquakes precisely but it would be deplorable if they were to resign themselves to modelling just the regular, gradual movements of tectonic plates. Instead they have developed ways of mapping the evolution of stress patterns, identifying areas at risk and refining heuristics for hazard assessment. Why not do the same for the economy?

数据收集会引起隐私方面的担忧,但这必不可少。地震学家可能无法准确地预测地震,他们若只是将构造板块的正常而又缓慢的移动做成模型,那就可悲可叹了。他们继续努力,绘制出了地壳应力分布演变图,能够找出地震多发带,精确进行风险评估。我们为何不在经济方面也做通样的尝试呢?
 

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