Management of financial risks and sound decision making rely on the accurate information and predictive models. Drawing useful information efficiently from big data with complex structures and building accurate models are therefore crucial tasks. Most commonly used methods for statistical...

Management of financial risks and sound decision making rely on the accurate information and predictive models. Drawing useful information efficiently from big data with complex structures and building accurate models are therefore crucial tasks. Most commonly used methods for statistical...

Norway experienced a high immigration flow after the EEA directive in 2004 stating workers right to free movement within the European Union and EEA-countries. There is no clear consensus in the literature on how immigration affects native wages, but some studies using Norwegian micro data have...

Norway experienced a high immigration flow after the EEA directive in 2004 stating workers right to free movement within the European Union and EEA-countries. There is no clear consensus in the literature on how immigration affects native wages, but some studies using Norwegian micro data have...

Norway experienced a high immigration flow after the EEA directive in 2004 stating workers right to free movement within the European Union and EEA-countries. There is no clear consensus in the literature on how immigration affects native wages, but some studies using Norwegian micro data have...

Norway experienced a high immigration flow after the EEA directive in 2004 stating workers right to free movement within the European Union and EEA-countries. There is no clear consensus in the literature on how immigration affects native wages, but some studies using Norwegian micro data have...

Pareto processes are suitable to model stationary heavy-tailed data. Here, we consider the auto-regressive Gaver-Lewis Pareto Process and address a study of the tail behavior. We characterize its local and long-range dependence. We will see that consecutive observations are asymptotically tail...

Pareto processes are suitable to model stationary heavy-tailed data. Here, we consider the auto-regressive Gaver–Lewis Pareto Process and address a study of the tail behavior. We characterize its local and long-range dependence. We will see that consecutive observations are asymptotically tail...

This paper focuses on two applications of time series methods. The first proposes a simple transformation of the unit root form of stationary testing to infer about the validity of smoothing by second-order running averages of a series, or of the variables in a linear model (here opposing...