Classical and Bayesian Estimation of the AR(1) Model with Skew-Symmetric Innovations

This paper considers a first-order autoregressive model with skew-normalinnovations from a parametric point of view. we develop an essential theory for com-puting the maximum likelihood estimation of model parameters via an expectation-maximization (em) algorithm. also, a bayesian method is proposed to estimate theunknown parameters of the model. the eciency and applicability of the proposedmodel are assessed via a simulation study and a real-world example.