REVISITING THE CANADIAN LYNX TIME SERIES ANALYSIS THROUGH TARMA MODELS

The class of threshold autoregressive models has been proven to be a powerful and appropriate tool to describe many dynamical phenomena in different fields. In this work, we deploy the threshold autoregressive moving-average framework to revisit the analysis of the benchmark Canadian lynx time series. This data set has attracted great attention among non-linear time series analysts due to its asymmetric cycle that makes the investigation very challenging. We compare some of the best threshold autoregressive models (TAR) proposed in literature with a selection of threshold autoregressive moving-average models (TARMA). The models are compared under different prospectives: (i) goodness-of-fit through information criteria, (i i) their ability to reproduce characteristic cycles, (i v) their capability to capture multimodality and (i ii) forecasting performance. We found TARMA models that perform better than TAR models with respect to all these aspects.