Normality and independence of error terms is a typical assumption for partial linear models. However, such an assumption may be unrealistic on many fields such as economics, finance and biostatistics. In this paper, we develop a Bayesian analysis for partial linear model with first-order autoregressive errors belonging to the class of scale mixtures of normal (SMN) distributions. The proposed model provides a useful generalization of the symmetrical linear regression models with independent error, since the error distribution cover both correlated and thick-tailed distribution, and has a convenient hierarchical representation allowing to us an easily implementation of a Markov chain Monte Carlo (MCMC) scheme. In order to examine the robustness of this distribution against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler (K-L) divergence. The proposed methodology is applied to the Cuprum Company monthly returns.

This content is only available via PDF.
You do not currently have access to this content.