The researchers developed a new model called multiple periodic autoregression, which extends the idea of periodic autoregression to multiple variables. They used periodic functions to describe how variables change over time and considered two versions of the model with different types of variance matrices. By applying the Bayes approach, they were able to estimate parameters and test statistical hypotheses. The main findings of the study show that this new model can accurately capture the complex relationships between variables that change periodically over time.