Title: Statistical boosting in Markov-switching generalized additive models for location, scale and shape
Authors: Timo Adam - Bielefeld University (Germany) [presenting]
Andreas Mayr - University of Bonn (Germany)
Thomas Kneib - University of Goettingen (Germany)
Abstract: A novel class of flexible latent-state time series regression models is proposed which is called Markov-switching generalized additive models for location, scale and shape. In contrast to conventional Markov-switching regression models, the presented methodology allows us to model state-dependent parameters of the response beyond the mean - including variance, skewness and kurtosis parameters - as potentially smooth functions of a given set of explanatory variables. In addition, the set of possible distributions that can be specified for the response is not limited to the exponential family but additionally includes, for instance, a variety of Box-Cox-transformed, zero-inflated and mixture distributions. We derive a novel EM algorithm and demonstrate how statistical boosting can be exploited to prevent overfitting while simultaneously performing variable selection. The suggested approach is assessed in simulation experiments and illustrated in a real-data setting, where we model the conditional distribution of the daily average price of energy in Spain over time.