Building on the framework put forward by Delli Gatti et al. 2011, in this paper we present and discuss a Macroeconomic Agent-Based Model with Capital and Credit (hereafter CC-MABM). The novelty of this model with respect to the previous framework consists in the introduction of capital goods...

This paper proposes tests of policy ineffectiveness in the context of macroeconometric rational expectations models. It is assumed that there is a policy intervention that takes the form of changes in the parameters of a policy rule, and that there are sufficient observations before and after...

This paper introduces two co-movement measures based on the Thick Pen Transform into the macroeconomic literature: the Thick Pen Measure of Association (TPMA) as well as Multi-Thickness Thick Pen Measure of Association (MTTPMA). Both measures are non-parametric, time-varying, and flexible....

This paper merges two branches of the literature. On one hand we study a heterogeneous agents framework to model exchange rates and stock prices. On the other hand we model the relationship between these two series through a DSGE model. Investors choose one of two rules to form their...

Agents forming adaptive expectations generally make systematic mistakes. This characterization has fostered the rejection of adaptive expectations in macroeconomics. Experimental evidence, however, shows that in complex environments human subjects frequently rely on adaptive heuristics –...

Experimental and empirical findings suggest that non-pecuniary motivations play a significant role as determinants of taxpayers' decision to comply with the tax authority and shape their perceptions and assessment of the tax code. By contrast, the canonical optimal income taxation model focuses...

We harness big data to detect prime locations - large clusters of knowledge-based tradable services - in 125 global cities and track changes in the within-city geography of prime service jobs over a century. Historically smaller cities that did not develop early public transit networks are less...