Agent-Based and DSGE Macroeconomic Models: A Comparative Study

本项目旨在比较和结合两种主要的建模方法：动态随机一般均衡(DSGE)模型和基于代理(AB)的模型。前者可以说是关于国家经济行为的最好的、最被广泛接受的、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件的复杂社会系统的最灵活的工具。这两种建模方法各有优缺点。动态随机一般均衡模型可能夸大了个体的理性和远见，而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进行交互的方式。AB模型通过定义理性、信息和远见有限的个体异质主体的特征和行为，为个体主体之间的其他社会互动在经济中的作用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，因为它们通常只对远离最佳选择且随时间演变的简单策略进行建模。问题是，代理人可能以无数种方式背离理性，导致一些经济学家所说的“荒野”。理性期望在动态随机一般均衡模型中的逻辑凝聚力可以作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两种建模方法并得出各自可以从对方那里学到什么的结论。我们的总体理念是颂扬和利用宏观经济模型的多样性。我们的研究策略是建立与DSSGE模型对应的AB模型，以揭示这两种建模方法的相对优势和局限性。我们的代理动态随机一般均衡(A-DSSGE)模型将为加强动态随机一般均衡模型的基础提供重要的见解，同时比传统的动态随机一般均衡模型更能从经济理论中获得信息。在经验上，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，使我们能够在传统的DSGE模型和我们的代理之间进行可能性竞赛。该项目有三个具体的相互关联的研究部分。首先，我们将以传统的新凯恩斯主义的DSGE模型为基准，通过放松对具有完美信息和认知能力的代表性主体的假设，构建一个与AB模型更接近的DSGE模型。这将涉及对信息限制进行建模的多种方法，包括传统方法以及与“理性疏忽”和“粘性信息”文献有关的方法。我们将调查代理人的异质性如何有助于解释现实世界的特征和影响政策规定。第二，我们将系统地比较我们的数据拟合方面的最佳的DSGE模型与在代理人、市场和开放方面具有相同经济结构的AB宏观经济模型。我们打算利用现有的估计动态随机一般均衡模型的方法来估计我们的资产负债表模型，从而使我们能够在传统的动态随机一般均衡模型和我们的代理模型之间进行“可能性竞赛”(最大化在所提供的模型中观察到选定的宏观经济数据的概率)。最后，我们将探索这两种不同的建模方法的稳健政策。例如，我们有可能设计出在所有竞争模型中都简单而稳健的利率规则。使用这一强有力的政策设计方法，我们提出宏观经济政策建议的目的是避免在建立预期模型、偏离决策规则和汇总的合理性方面成为“单一观点的囚徒”。

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.