Suchergebnisse

Geographic clustering of innovative industries is associated with the entry and success of spinoff firms. We develop a model to explain the multiple empirical patterns regarding cluster growth and spinoff formation and performance, without relying on agglomeration externalities. Clustering naturally follows from spinoffs locating near their parents. In our model, firms grow and spinoffs form through the discovery of new submarkets based on innovation. Rapid and successful innovation creates more opportunities for spinoff entry and drives a region's growth. Our model provides baseline estimates of levels of agglomeration that can be attributed to this process of innovation and spinoff formation.

The dynamic clustering (DC) algorithm is a method for discovering clusters in a given population. Unfortunately the classical DC algorithms fail to perform well in the presence of outliers. A robust dynamic clustering (RDC) algorithm is introduced to overcome this problem. Robust estimates of the location vector and the covariance matrix are calculated in the affine invariant case. A simulation study is presented to demonstrate the basic difference between the DC and the RDC algorithms. Three kinds of optimization criteria are used in case of contaminated multivariate normal distributions.