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In statistics, Smooth Transition Autoregressive models are typically applied to time series data as an extension of autoregressive models, in order to allow for higher degree of flexibility in model parameters through a smooth transition.
Given a time series of data xt, the STAR model is a tool for understanding and, perhaps, predicting future values in this series, assuming that the behaviour of the series changes depending on the value of the transition variable. The transition might depend on the past values of the x series , or exogenous variables.
The model consists of 2 autoregressive parts linked by the transition function. The model is usually referred to as the STAR models proceeded by the letter describing the transition function and p is the order of the autoregressive part. Most popular transition function include exponential function and first and second-order logistic functions. They give rise to Logistic STAR and Exponential STAR models.