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

Abstract

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.