The article develops a new way to model nonlinear autoregressive processes by treating them as linear models with changing coefficients. They use a method that updates parameters based on the predictive likelihood function score at each time point. The resulting model is compared to other types and is shown to be optimal for capturing time-varying and nonlinear relationships. The study demonstrates that this model performs well in extracting these relationships from real-world data on weekly unemployment insurance claims.