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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

基本信息

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

项目摘要

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.

本项目旨在比较和联合收割机两种主要的建模方法：动态随机一般均衡（DSGE）和基于代理（AB）的模型。前者可以说是关于国家经济行为的最佳结构、最广泛接受和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的组成部分的复杂社会系统的最灵活的工具。这两种建模方法有不同的优点和缺点。DSGE模型可能夸大了个体的理性和远见，低估了异质性的重要性，即主体之间的差异主要集中在经济主体通过总价格相互作用的方式上。AB模型通过定义具有有限理性、信息和远见的个体异质代理的特征和行为，为经济中个体代理之间的其他社会互动提供了一种更灵活的方法。另一方面，AB模型可能会夸大个人决策中的错误，因为它们通常只模拟远离最佳选择并随时间演变的简单策略。问题是，行为人可以以无数种方式背离理性，导致一些经济学家所说的“野性”。DSGE模型中理性预期的逻辑内聚性可以成为对学习和有限理性感兴趣的研究人员的基准。我们相信，这个项目将是第一个系统地比较这两种建模方法并得出结论的项目。我们的总体理念是庆祝和利用宏观经济模型的多样性。我们的研究策略是制定AB模型对应的DSGE模型，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，同时比传统的AB模型更好地了解经济理论。从经验上讲，我们将探索AB模型如何从DSGE模型中使用的估计方法中受益，使我们能够在传统DSGE模型和我们的代理模型之间进行似然竞赛。首先，我们将以传统的新凯恩斯主义DSGE模型为基准，通过放松具有完美信息和认知能力的代表性代理人的假设，构建更接近AB模型的DSGE模型。这将涉及多种方式的建模信息的限制，包括传统的方法，以及那些与“理性疏忽”和“粘性信息”的文献。我们将研究主体的异质性如何有助于解释真实的世界特征并影响政策处方。其次，我们将系统地比较我们的“最佳”DSGE模型与AB宏观经济模型的数据拟合，后者在主体、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计DSGE模型的方法来估计我们的ABM模型，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“似然竞赛”（最大化观察所选宏观经济数据的概率）。例如，我们可以设计出简单而稳健的利率规则，使之适用于所有竞争模型。利用这一强有力的政策设计方法，我们的宏观经济政策建议旨在避免在预期建模、决策规则偏离合理性和总体方面成为“单一前景的囚徒”。