Journal of Economic Interaction and Coordination最新文献

Although various indicators of economic sentiment are often assessed in macroeconomic studies, the generating process of economic sentiment itself is still a puzzle. This paper offers pioneer evidence on the persistence of economic sentiment. Applying a battery of fractional integration tests on the European Economic Sentiment Indicator (ESI) of all individual EU member states, we reveal that ESI is dominantly a long-memory process. This finding is robust across several estimators, and it fairly contradicts the conventional wisdom of ESI as a purely transitory macroeconomic shock. Further on, this is true for both core EU economies and new member states, although the later ones exhibit slightly longer memory. Finally, we reveal that the end of the Great Moderation era has increased ESI's persistence, but the effect is only marginal. As it seems, a series of macroeconomic turbulences recorded after the global financial crisis has not initiated a significant shift in agents' collective memory and ESI will likely keep its pivotal role in governing business cycles in the future.

We develop an agent-based model of vaccine decisions across a heterogeneous network model with urban and rural regions. In the model, agents make rational decisions to vaccinate or not, based on the relative private costs of vaccinations and infections as well as an estimated probability of infection if not vaccinated. The model is a methodological advance in that it provides an economic rationale for traditional threshold models of vaccine decision-making that are commonly used in agent-based network models of vaccine choice. In the model, more dense urban regions have more connections between agents than less dense rural regions. Higher density leads to higher levels of vaccine usage and lower rates of infection in urban regions within the model. This finding adds to the more commonly discussed socio-economic reasons for higher levels of vaccination usage in urban areas compared to rural areas. In addition to this direct contribution, the paper emphasizes the importance of endogenous decision-making in models of epidemiology. For instance, we find that networks that lead to larger epidemics in exogenous vaccination models lead to smaller epidemics in our model because agents use vaccinations to offset the additional risk introduced by these network structures. Endogenous agent responses to risk need to be incorporated into theoretical and empirical models of economic epidemiology.

In this review paper, we aim to make the case that a concept from retail analytics and marketing-the customer journey-can provide promising new frameworks and support for agent-based modeling, with a broad range of potential applications to high-resolution and high-fidelity simulation of dynamic phenomena on urban high streets. Although not the central focus of the review, we consider agent-based modeling of retail high streets against a backdrop of broader debate about downtown vitality and revitalization, amid a climate of economic challenges for brick-and-mortar retail. In particular, we consider how agent-based modeling, supported by insights from consideration of indoor shopping, can provide planning and decision support in outdoor high street settings. Our review considers abstractions of customers through conceptual modeling and customer typology, as well as abstractions of retailing as stationary and mobile. We examine high-level agency of shop choice and selection, as well as low-level agency centered on perception and cognition. Customer journeys are most often trips through geography; we therefore review path-planning, generation of foot traffic, wayfinding, steering, and locomotion. On busy high streets, journeys also manifest within crowd motifs; we thus review proximity, group dynamics, and sociality. Many customer journeys along retail high streets are dynamic, and customers will shift their journeys as they come into contact with experiences and service offerings. To address this, we specifically consider treatment of time and timing in agent-based models. We also examine sites for customer journeys, looking in particular at how agent-based models can provide support for the analysis of atmospherics, artifacts, and location-based services. Finally, we examine staff-side agency, considering store staff as potential agents outdoors; and we look at work to build agent-based models of fraud from customer journey analysis.

We analyze the impact of different designs of COVID-19-related lockdown policies on economic loss and mortality using a micro-level simulation model, which combines a multi-sectoral closed economy with an epidemic transmission model. In particular, the model captures explicitly the (stochastic) effect of interactions between heterogeneous agents during different economic activities on virus transmissions. The empirical validity of the model is established using data on economic and pandemic dynamics in Germany in the first 6 months after the COVID-19 outbreak. We show that a policy-inducing switch between a strict lockdown and a full opening-up of economic activity based on a high incidence threshold is strictly dominated by alternative policies, which are based on a low incidence threshold combined with a light lockdown with weak restrictions of economic activity or even a continuous weak lockdown. Furthermore, also the ex ante variance of the economic loss suffered during the pandemic is substantially lower under these policies. Keeping the other policy parameters fixed, a variation of the consumption restrictions during the lockdown induces a trade-off between GDP loss and mortality. Furthermore, we study the robustness of these findings with respect to alternative pandemic scenarios and examine the optimal timing of lifting containment measures in light of a vaccination rollout in the population.

This paper extends the conventional DSGE literature by developing a New Keynesian DSGE model featuring imperfect financial markets with various friction costs, which allows for the study of macroeconomic dynamics under different levels of financial integration. We conduct Bayesian estimation and draw implications on the macroeconomic effects of gradual financial integration using the Chinese economy as an example. We find that macroeconomic fluctuations vary with different levels of financial integration and the specific relationship depends on the nature of exogenous shocks. Variance decomposition analysis shows that as financial integration increases, the contribution of foreign exchange shocks declines while that of domestic shocks increases. We also find that there is a notable enhancement of welfare associated with improvement in financial integration, and the effectiveness of monetary policy in emerging market economies would be weakened as financial integration increases.