Agent-Based and DSGE Macroeconomic Models: A Comparative Study

基于主体和 DSGE 宏观经济模型：比较研究

• 批准号: ES/K005154/1
• 负责人: Paul Levine
• 金额: $ 64.61万
• 依托单位: University of Surrey
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FK005154%2F1
• 关键词: Agent; Based; DSGE; Macroeconomic; Models

This project aims to compare and combine two major approaches to modelling: Dynamic Stochastic General Equilibrium (DSGE) and Agent-Based (AB) models. The former is arguably the best structured, most widely accepted, most sophisticated source of empirical information on the behaviour of national economies. The latter is arguably the most flexible tool for studying complex social systems with many dynamic and interacting components.The two modelling approaches have different advantages and disadvantages. DSGE models may exaggerate individual rationality and foresight, and understate the importance of heterogeneity, that is, differences between agents focusing mostly on the way economic agents interact through aggregate prices. The AB models offer a more flexible approach to the role of other social interactions between individual agents in the economy by defining the characteristics and behaviour of individual heterogeneous agents with limited rationality, information and foresight. On the other hand, AB models may exaggerate errors in individual decision-making, since they usually model only simple strategies that are far from optimal choices and that evolve in time. The problem is that agents can depart from rationality in an infinite number of ways leading into what some economists refer to as a `wilderness'. The logical cohesion of rational expectations in DSGE models can be a benchmark for researchers interested in learning and bounded rationality.This project will, we believe, be the first to compare the two modelling approaches systematically and draw conclusions on what each can learn from the other. Our general philosophy is to celebrate and exploit diversity in macroeconomic models. Our research strategy is to formulate AB model counterparts of DSGE models to reveal the relative strengths and limitations of the two modelling approaches. Our agentised DSGE (A-DSGE) models will provide important insights for strengthening the foundations of DSGE models while being better informed by economic theory than conventional AB models. Empirically, we will explore how AB models can benefit from the estimation approaches used in DSGE models, enabling us to perform a likelihood race between the traditional DSGE model and our agentised ones.The project has three specific inter-related components of the research. First, we will take a conventional New Keynesian DSGE model as benchmark and build a DSGE model that more closely matches an AB model by relaxing the assumption of a representative agent with perfect information and cognitive abilities. This will involve multiple ways of modelling information limitations including the traditional approaches, as well as those associated with the `rational inattention' and `sticky information' literatures. We will investigate how the heterogeneity of agents can help to explain real world features and affect policy prescriptions.Second, we will systematically compare our `best' DSGE model in terms of data fit with an AB macroeconomic model counterpart that has the same economic structure in terms of agents, markets and openness. We intend to draw upon existing methods of estimating DSGE models to estimate our ABM models, thus enabling us to perform a `likelihood race' (that maximizes the probability of observing selected macroeconomic data across the models on offer) between the traditional DSGE model and our agentized ones.Finally, we will explore robust policies across these two contrasting modelling approaches. It is possible to design, for example, interest rate rules that are simple and robust across the rival models. Using this robust policy design methodology, our aim for macroeconomic policy recommendations is to avoid becoming `a prisoner of a single outlook' with respect to the modelling of expectations, departures from rationality in decision rules and aggregation.

该项目旨在比较和结合两种建模的主要方法：动态随机通用平衡（DSGE）和基于代理（AB）模型。前者可以说是关于国民经济行为的最佳结构，最广泛，最复杂的经验信息来源。后者可以说是研究具有许多动态和相互作用组件的复杂社会系统的最灵活的工具。两种建模方法具有不同的优势和缺点。 DSGE模型可能会夸大个人理性和远见，并低估了异质性的重要性，即，主要关注经济代理商通过总价格相互作用的方式之间的差异。 AB模型通过定义具有有限理性，信息和远见的个别异质代理人的特征和行为，为经济中各个代理人之间其他社会互动的作用提供了更灵活的方法。另一方面，AB模型可能会夸大个人决策的错误，因为它们通常仅建模远非最佳选择且随时间发展的简单策略。问题在于，代理商可以以无限的方式偏离理性，导致一些经济学家称为“荒野”。 DSGE模型中理性期望的逻辑凝聚力对于对学习和有限理性感兴趣的研究人员可能是一个基准。我们认为，这个项目将是第一个系统地比较两种建模方法并得出关于每个人都可以学习的东西的结论。我们的一般哲学是庆祝和利用宏观经济模型中的多样性。我们的研究策略是制定DSGE模型的AB模型对应物，以揭示两种建模方法的相对优势和局限性。我们的代理DSGE（A-DSGE）模型将为加强DSGE模型的基础提供重要的见解，同时比传统AB模型更好地了解经济理论。从经验上讲，我们将探讨AB模型如何从DSGE模型中使用的估计方法中受益，从而使我们能够在传统的DSGE模型和我们的代理模型之间进行可能性种族。该项目具有研究的三个特定相关组成部分。首先，我们将以常规的新凯恩斯主义DSGE模型为基准，并建立一个DSGE模型，该模型通过放宽具有完美信息和认知能力的代表代理的假设来更加与AB模型相匹配。这将涉及多种建模信息限制的方式，包括传统方法以及与“理性不注意”和“粘性信息”文献相关的方法。我们将调查代理的异质性如何帮助解释现实世界的特征并影响政策处方。第二，我们将系统地将我们的“最佳” DSGE模型与AB宏观经济模型相交，该模型在代理商，市场和开放性方面具有相同的经济结构。我们打算利用现有的估算DSGE模型来估计ABM模型的方法，从而使我们能够执行“可能性竞赛”（这最大程度地观察传统DSGE模型和我们的代理模型之间的跨模型中选定的宏观经济数据的可能性）。我们将在这些相反的模型中探索强大的策略。例如，可以设计在竞争对手模型中简单且坚固的利率规则。使用这种强大的政策设计方法，我们对宏观经济政策建议的目的是避免成为期望建模，偏离决策规则和汇总的理性的“单一观点的囚犯”。

项目成果

Reinforcement Learning in a New Keynesian Model

新凯恩斯主义模型中的强化学习

• DOI: 10.3390/a16060280
• 发表时间: 2023
• 期刊: Algorithms
• 影响因子: 2.3
• 作者: Deák S

Agent-based Macroeconomics: What have we learned?

基于主体的宏观经济学：我们学到了什么？

• 作者: Dilaver Kalkan, O

Deep versus superficial habit; its all in the persistence

深层习惯与肤浅习惯；

• 作者: Cristiano Cantore (Author)

Learning, heterogeneity, and complexity in the New Keynesian model

新凯恩斯主义模型中的学习、异质性和复杂性

• DOI: 10.1016/j.jebo.2019.07.014
• 发表时间: 2019
• 期刊: Journal of Economic Behavior & Organization
• 影响因子: 2.2
• 作者: Calvert Jump R

Designing Robust Policies using Optimal Pooling

使用最优池设计稳健的策略

• 作者: Deak S