Spline-based nonparametric inference in general state-switching models

State-switching models combine immense flexibility with relative mathematical simplicity and computational tractability and, as a consequence, have established themselves as general-purpose models for time series data. In this paper, we provide an overview of ways to use penalized splines to allow for flexible nonparametric inference within state-switching models, and provide a critical discussion of the use of corresponding classes of models. The methods are illustrated using animal acceleration data and energy price data.