Linear quantile autoregressive model estimation using Stochastic EM algorithm

In this paper, the quantile autoregressive time series model is introduce and then the model parameters are estimated using the Stochastic EM algorithm, which is an iterative method to compute maximum likelihood estimates. The likelihood function for the quantile autoregressive model is constructed based on the asymmetric Laplace distribution and a scale mixture representation of this distribution is used to estimate the model parameters via Stochastic EM algorithm.The efficiency and application of the proposed method are illustrated by some simulation studies and analyzing a real dataset.