On forecasting with univariate autoregressive processes: a bayesian approach

1 January 1984

journal article
research article
Published by Taylor & Francis in Communications in Statistics - Theory and Methods

Vol. 13 (11) , 1305-1320
https://doi.org/10.1080/03610928408828758

Abstract

Using a normal-gamma prior density for the parameters of a p-th order autoregressive process, the Bayesian predictive density of k future observations is derived and it is shown that it is the product of k univariate t densities. Our results are illustrated with one step ahead forecasts employing AR(1) and AR(2) models with a vague prior density for the parameters.

Keywords

This publication has 3 references indexed in Scilit:

Bayesian Forecasting
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1976
Statistical Prediction Analysis
Published by Cambridge University Press (CUP) ,1975
Multiparameter Problems From a Bayesian Point of View
The Annals of Mathematical Statistics, 1965