In this paper business cycles are considered as a multivariate phenomenon and not as a univariate one determined e.g. by the GNP. The subject is to look for the number of phases of a business cycle, which can be motivated by the number of clusters in a given dataset of macro-economic variables....

health disparities, health behaviors, dynamic demand, side effects, structural models, HIV/AIDSaThis paper analyzes the stability and distribution of ambiguity attitudes using a broad population sample. Using high-powered incentives, we collected six waves of data on ambiguity attitudes about...

Data from a range of different environments indicate that the incidence of death is not randomly distributed across families but, rather, that there is a clustering of death amongst siblings. A natural explanation of this would be that there are (observed or unobserved) differences across...

The economic expansion of the late 1990s created many opportunities for business creation in Silicon Valley, but the opportunity cost of starting a business was also high during this period because of the exceptionally tight labor market. A new measure of entrepreneurship derived from matching...

Building homogenous classes is one of the main goals in clustering. Homogeneity can be measured by the intra-class variance (Bock, 1998). Especially in erosion projects but in other applications as well the separation between the built classes is as important as the homogeneity of the classes....

We study the impact of graduating in a recession in Flanders (Belgium), i.e. in a rigid labor market. In the presence of a high minimum wage, a typical recession hardly influences the hourly wage of low educated men, but reduces working time and earnings by about 4.5% up to twelve years after...

When analyzing business cycle data, one observes that the relevant predictor variables are often highly correlated. This paper presents a method to obtain measures of importance for the classification of data in which such multicollinearity is present. In systems with highly correlated variables...

The optimization of the hyper-parameters of a statistical procedure or machine learning task is a crucial step for obtaining a minimal error. Unfortunately, the optimization of hyper-parameters usually requires many runs of the procedure and hence is very costly. A more detailed knowledge of the...

Linear Discriminant Analysis (LDA) performs well for classifica- tion of business phases even though the premises of an LDA are not met. As the variables are highly correlated there are numerical as well as interpretational shortcomings. By transforming the classification problem to a regression...