Robust Bayesian analysis for autoregressive models

Abstract

Time series data sometimes show violation of normal assumptions. For cases where the assumption of normality is untenable, more flexible models can be adopted to accommodate heavy tails. The exponential power distribution (EPD) is considered as possible candidate for errors of time series model that may show violation of normal assumption. Besides, the use of flexible models for errors like EPD might be able to conduct the robust analysis. In this paper, we especially consider EPD as the flexible distribution for errors of autoregressive models. Also, we represent this distribution as scale mixture of uniform and this form enables efficient Bayesian estimation via Markov chain Monte Carlo (MCMC) methods.

Bayesian estimation of median household income for small areas with some longitudinal pattern

“…We consider Bayesian Fay-Herriot type small area models which include time-specific random effect to accommodate any unspecified time varying income pattern during five years. Current works in Bayesian modeling is related to Goo and Kim (2013) and Ryu and Kim (2015).…”