A0323
Title: Are information criteria good enough to choose the right the number of regimes in hidden Markov models?
Authors: Bouchra Nasri - University of Montreal (Canada)
Bruno N Remillard - HEC Montreal (Canada)
MamadouYamar Thioub - HEC Montreal (Canada) [presenting]
Abstract: Selecting the number of regimes in hidden Markov models is an important problem. Many criteria are used to do so, such as Akaike information criterion (AIC), Bayesian information criterion (BIC), integrated completed likelihood (ICL), deviance information criterion (DIC), and Watanabe-Akaike information criterion (WAIC), to name a few. Goodness-of-fit tests are introduced for general hidden Markov models with covariates, where the distribution of the observations is arbitrary, i.e., continuous, discrete, or a mixture of both. A selection procedure is proposed based on this goodness-of-fit test. The main aim is to compare the classical information criterion with the new criterion when the outcome is either continuous, discrete, or zero-inflated. Numerical experiments assess the finite sample performance of the goodness-of-fit tests, and comparisons between the different criteria are made.
