Volume 228, Issue 2-3

This special issue of the Journal of Economics and Statistics is devoted to the use of agent-based models for economic policy advice. It presents a collection of research papers in different fields of applications. Special emphasis is laid on discussing the potential and possible limitations of agent-based models for economic policy advice. The editorial provides an overview on the role of agent-based modeling in economic policy referring also to the papers presented. Furthermore, it highlights the strength of the approach, i.e., the explicit microfoundation and the modeling of heterogenous agents. Finally, we also report on current limitations of the method with regard to economic policy advice and point at some areas deserving further research.

This paper discusses the use of agent-based simulation models for regulatory advice in electricity market regulation. It briefly introduces the necessary procedures and the state-of-the-art of the methodology, and outlines its possible range of application. In a second part, the paper presents an agent-based simulation model developed by the authors. The model can be applied for analyzing different market designs and market structures in order to derive evidence for regulatory advice. This is exemplified through the analysis of two settlement rules in the balancing power market and of several divestiture scenarios of the German electricity sector.

Tolls are frequently discussed policies to reduce traffic in cities. However, road pricing measures are seldom implemented due to high investments and unpopularity. Transportation planning tools can support planning authorities by solving those problems if they take into account the following aspects: – Demographic attributes like income and time constraints – Time reactions to the policy – Schedule changes of population’s individuals during the whole day Our approach uses multi-agent simulations to model and simulate full daily plans. Each of our agents has a utility function that appraises the performance of a typical, microscopically simulated day. The sum of all utility changes to a policy change can be interpreted as the change in the system’s welfare thus the economic evaluation of a measure straightforward. The approach is tested with travel behavior of the Zurich metropolitan region in Switzerland. Several tolling schemes are investigated. It is shown that the simulation can be used to model travelers’ reactions to time-dependent tolls in a way most existing transportation planning tools are not able to do. It is demonstrated that route adjustment only, as is done in many traditional transport planning packages, results in no economic gains from the tolls. As time-dependent tolls are a much-debated subject in transportation politics, the ability to fully model such tolls and the reactions of travelers may help to find better toll schemes. In a world where individuals have more and more freedom to schedule their daily plans, agent-based simulations offer an intuitive way to research complex topics with lots of interdependencies.

Models with heterogeneous interacting agents have proven to be quite successful in the past. For instance, such models are able to mimic the dynamics of financial markets quite well. The goal of our paper is to explore whether this approach may offer new insights into the working of certain regulatory policies such as transaction taxes, central bank interventions and trading halts. Although this strand of research is rather novel, we argue that agent-based models may be used as artificial laboratories to improve our understanding of how regulatory policy tools function.

We propose a novel heterogeneous interacting agents model in which traders are allowed to select endogenously between two different forecasting models and are moreover allowed to be short-term speculators or long-term investors. Within this model framework we study the effects of currency transaction taxes on exchange rate volatility and traders’ behavior measured by their population fractions. The numerical analysis yields the result that these taxes reduce the variance of exchange rate returns, but increase that kurtosis.Moreover it does not lead to a reduction in the misalignment. The second result is, the tax harms short-term speculation in favor of long-term investments, while it also harms trading rules based on economic fundamentals in favor to trend-extrapolating trading rules. But these results are only valid if agents trade very aggressively. Otherwise taxation is not necessary.

We develop an agent-based macroeconomic model featuring a distinct geographical dimension and heterogeneous workers with respect to skill types. The model, which will become part of a larger simulation platform for European policymaking (EURACE), allows us to conduct exante evaluations of a wide range of public policy measures and their interaction. In particular, we study the growth and labor market effects of various policy types that promote workers’ general skill levels. Using a calibrated model it is examined in how far effects differ if spending is uniformly spread over all regions in the economy or focused in one particular region.We find that the geographic distribution of policy measures significantly affects the effects of the policy even if total spending is kept constant. Focussing training efforts in one region is the worst policy outcome while spreading funds equally across regions generates a larger output in the long-run but not in the short-run.

Macroeconomic policy analysis is a challenge for agent-based models because these types of model are generally much elaborated on the specific market levels for partial (micro) markets, but have been of limited use for macroeconomic policy issues due to calibration and “model closure” issues.Moreover, macroeconomic policy measures at a high level of aggregation, such as general fiscal policy and monetary policy, tend to include several microeconomic aspects determined by the macroeconomic policy makers (i.e. the specific process of money transmission, budget constraints within/for the public sector, etc.), which are not usually captured by agent-based models with an emphasis on microfoundation. Thus, a fully-specified macroeconomic agent-based model, AS1, is applied in this paper. Specifically, the monetary sector is modeled in detail, and both the central bank and the public sector are set up as separate agents with their own expectations and behavior. The paper has two aims: (a) to show that economic policy may be analyzed in this context with more elaborate expectation formation mechanisms than in traditional models, and (b) to demonstrate that this might change the assessment of policy effectiveness. Two illustrative examples for monetary and fiscal policies are presented with different levels of rationality and differences in the expectation formation process.