Title: Methodological advances in slash distributions
Authors: Inmaculada Barranco-Chamorro - Universidad de Sevilla (Spain) [presenting]
Abstract: In real-world data, it is quite common to find symmetrical and unimodal histograms with heavy tails that do not fit well to a normal distribution. Slash models are a good option to deal with this kind of situations, in which departures of Gaussianity are a serious problem for the data analyst. This is one the main reasons why slash distributions have received a great deal of attention during the last decades. Symmetrical and unimodal slash models are discussed. These models are obtained as the quotient of two independent continuous random variables. Slash distributions are models with heavy tails, and three parameters (location, scale and kurtosis). The emphasis is on the estimation of kurtosis parameter. It is shown that traditional inference methods of estimation fail in slash models. Some improvements are proposed. A practical application to real data, of interest in economics, is included.