Agent-Based and DSGE Macroeconomic Models: A Comparative Study

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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.