Firms grow and decline by relatively lumpy jumps which cannot be accounted by the cumulation of small, âatom-lessâ, independent shocks. Rather âbigâ episodes of expansion and contraction are relatively frequent. More technically, this is revealed by the fat-tailed distributions of growth rates. This applies across different levels of sectoral disaggregation, across countries, over different historical periods for which there are available data. What determines such property? In Dosi et al. (The footprint of evolutionary processes of learning and selection upon the statistical properties of industrial dynamics. Industrial and corporate change. Oxford University Press, Oxford, 2016) we implemented a simple multi-firm evolutionary simulation model, built upon the coupling of a replicator dynamic and an idiosyncratic learning process, which turns out to be able to robustly reproduce such a stylized fact. Here, we investigate, by means of a Kriging meta-model, how robust such âubiquitousnessâ feature is with regard to a global exploration of the parameters space. The exercise confirms the high level of generality of the results in a statistically robust global sensitivity analysis framework.
- ABMs validation
- Business and International Management
- Economics and Econometrics
- Fat-tailed distributions
- Kriging meta-modeling
- Near-orthogonal latin hypercubes
- Variance-based sensitivity analysis