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This paper considers a first-order autoregressive model with skew-normal innovations from a parametric point of view. We develop an essential theory for computing the maximum likelihood estimation of model parameters via an Expectation-Maximization (EM) algorithm. Also, a Bayesian method is proposed to estimate the unknown parameters of the model. The efficiency and applicability of the proposed model are assessed via a simulation study and a real-world example.doi:10.29252/jirss.18.1.157 fatcat:gbx5ouecefdy5d4ag7z3bwsv5m