Animal Spirits, Lumpy Investment, and the Business Cycle

Empirical literature on investment and output dynamics is characterized by two robust stylized facts at the macro level. First, investment is considerably more volatile than output. Second, fluctuations of output and investment are highly synchronized. Furthermore, at the micro level, firm investment appears to be very lumpy. In this paper, we ask whether the two macroeconomic stylized facts above can be explained in terms of bounded rationality (i.e. "animal spirits") in firm investment behavior and the ensuing lumpiness in investment patterns. To address this question, we present an evolutionary, agent-based, model of industry dynamics and firm investment behavior. The economy is composed of consumers and firms, who belong to two industries. Firms in the first industry perform R&D and produce heterogeneous machine tools. Firms in the second industry invest in new machines and produce a consumption good. Lumpiness of firm investment is not grounded on non-convex adjustment costs, but on "animal spirits": manufacturing firms invest only if they expect a large growth in the demand for their product. Simulations show that the model is able to generate - as emergent properties - Keynesian endogenous business cycles and to reproduce the foregoing empirical macro output-investment regularities at the business cycle frequencies.Evolutionary Models, ACE Models, Animal Spirits, Lumpy Investment, Output Fluctuations, Endogenous Business Cycles

Are Output Growth-Rate Distributions Fat-Tailed? Some Evidence from OECD Countries

This work explores some distributional properties of aggregate output growth-rate time series. We show that, in the majority of OECD countries, output growth-rate distributions are well approximated by symmetric exponential power densities with tails much fatter than those of a Gaussian (but with finite moments of any order). Fat tails robustly emerge in output growth rates independently of: (i) the way we measure aggregate output; (ii) the family of densities employed in the estimation; (iii) the length of time lags used to compute growth rates. We also show that fat tails still characterize output growth-rate distributions even after one washes away outliers, autocorrelation and heteroscedasticity

AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model

This paper presents a dynamic agent-based model of land use and agricultural production under environmental boundaries, finite available resources and endogenous technical change. In particular, we model a spatially explicit smallholder farming system populated by boundedly-rational agents competing and innovating to fulfill an exogenous demand for food, while coping with a changing environment shaped by their production choices. Given the strong technological and environmental uncertainty, agents learn and adaptively employ heuristics which guide their decisions on engaging in innovation and imitation activities, hiring workers, acquiring new farms, deforesting virgin areas and abandoning unproductive lands. Such activities in turn impact farm productivity, food production, food prices and land use. We firstly show that the model can replicate key stylized facts of the agricultural sector. We then extensively explore its properties across several scenarios featuring different institutional and behavioral settings. Finally, we simulate the model across different applications considering deforestation and land abandonment; human-induced soil degradation; and climate impacts. AgriLOVE offers a flexible simulation environment to study the endogenous emergence of different agricultural production regimes from the interaction of spatially dispersed farms subject to resource constraints, spatial influence and climate change