Bayesian Dynamic models for non-linear auto-regressive processes

This research investigates alternative Bayesian approaches to the existing static smooth transition auto-regressive (STAR) models for auto-regressive non-linear time series. The approaches we propose are dynamic and analytic being thus suitable for non-stationary real-time AR processes such as those exhibiting asymmetric cycles and/or sudden changes of level and variability. Examples include unemployment rates, industrial production and hourly electricity consumption and prices data. 

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