Firms grow and decline by relatively lumpy jumps which cannot be accounted by the cumulation of small, "atom-less", independent shocks. Rather "big" episodes of expansion and contraction are relatively frequent. More technically, this is revealed by fat tail distributions of growth rates. This...

This paper provides evidence that learning about demand is an important driver of firms' dynamics. We present a simple model with Bayesian learning in which firms are uncertain about their idiosyncratic demand parameter in each of the markets they serve, and update their beliefs as noisy...

Analyzing a comprehensive database of limited liability manufacturing firms this paper investigates the relation between a firm’s financial situation and its conditional expected growth rate. Specifically, using quantile regressions, we obtain a quantitative characterization of this relation...

Using both regression analysis and an unsupervised graphical model approach (never applied before to this issue), we confirm the rejection of the Gibrat's law when our firm-level data are considered over the entire investigated period, while the opposite is true when we allow for market...

Using both regression analysis and an unsupervised graphical model approach (never applied before to this issue), we confirm the rejection of the Gibrat's law when our firm-level data are considered over the entire investigated period, while the opposite is true when we allow for market...

The distribution of firm sizes is known to be heavy tailed. In order to account for this stylized fact, previous studies have focused mainly on growth through investments in a company's own operations (internal growth). Thereby, the impact of mergers and acquisitions (M&A) on the firm size...