This paper offers new insights into the processes of firm growth by applying a reducedform vector autoregression (VAR) model to longitudinal panel data on French manufacturing firms. We observe the co-evolution of key variables such as growth of employment, sales, and gross operating surplus, as...

While Gibrat's Law assumes that growth rate variance is independent of size, empirical work has usually found a negative relationship between growth rate variance and firm growth. Using data on French manufacturing firms, we observe a relatively low, but statistically significant, negative...

We apply a reduced-form vector autoregression model to analyze the growth processes of Italian manufacturing firms, 1989-1997. We focus in particular on lead-lag associations describing the coevolution of employment growth, sales growth, growth of profits and labour productivity growth....

We investigate the lead-lag relationship between growth of patent applications, growth of R&D, and growth of total sectoral employment for 270 German labour market regions over the period 1999-2005. Our unique panel dataset includes information on four two-digit industries, namely Chemistry,...

Recent work drawing on data for large and small firms has shown a Pareto distribution of firm size. We mix a Gibrat-type growth process among incumbents with an exponential distribution of firm's age, to obtain the empirical Pareto distribution.

Structural vector-autoregressive models are potentially very useful tools for guiding both macro- and microeconomic policy. In this paper, we present a recently developed method for exploiting non-Gaussianity in the data for estimating such models, with the aim of capturing the causal structure...

This paper focuses on how consumer motivation can be tapped in order to encourage the adoption of cleaner technologies. Consumers are heterogeneous they may be guided by intrinsic motivation or extrinsic motivation. While information provision policies (such as the energy label for cars) may be...

Structural vector-autoregressive models are potentially very useful tools for guiding both macro- and microeconomic policy. In this paper, we present a recently developed method for exploiting non-Gaussianity in the data for estimating such models, with the aim of capturing the causal structure...

We investigate the lead-lag relationship between growth of patent applications, growth of R&D, and growth of total sectoral employment for 270 German labour market regions over the period 1999-2005. Our unique panel dataset includes information on four two-digit industries, namely Chemistry,...

We apply a reduced-form vector autoregression model to analyze the growth processes of Italian manufacturing firms, 1989-1997. We focus in particular on lead-lag associations describing the coevolution of employment growth, sales growth, growth of profits and labour productivity growth....