TAR Modeling with Missing Data when the White Noise Process Follows a Student’s t-Distribution (2015)
This paper considers the modeling of the threshold autoregressive (TAR) process, which is driven by a noise process that follows a Student’s t-distribution. The analysis is done in the presence of missing data in both the threshold process Zt and the interest process Xt. We develop a three-stage procedure based on the Gibbs sampler in order to identify and estimate the model. Additionally, the estimation of the missing data and the forecasting procedure are provided. The proposed methodology is illustrated with simulated and real-life data.