Bayesian Analysis Of Economic Time Series ARMA Model And Its Applicaton

In economic field, the time series models are important methods in describing and forecasting the objective economic process. However, when put them into application, because of the particularity of economic field, we often encounter many difficulties in the time series models analysis by using the traditional frequency statistical method. Therefore, this paper describes a technique, economic forecasting with Bayesian time series models, which has proved over the past several years to be an attractive alternative in many situations to the use of traditional economtric models or other time series techniques.This paper mainly deals with the estimated procedure for the robust ARMA model, the ARFIMA model and the VARFIMA model under Bayesian inference framework, and their application with posterior computations is performed by MCMC Method.Firstly, the Bayesian theory about the robust ARMA(p,q) model is explored.This part analysis the model's statistical structure and its likelihood function, constructs the parameters'prior distribution and makes an inference for the parameters'conditional posterior distribution; then through a series of 1949-2005 Chinese population data , implementes Bayesian robust ARMA(p,q) model simulation with WinBUGS. Secondly, we analyzes ARFIMA(p,d,q) model with Bayesian method.We analysis the model's statistical structure and its likelihood function, construct the parameters'prior distribution and make an inference for the parameters'conditional posterior distribution. Examples with simulated and actual economic data are presented.Finally, the Bayesian inference theory about the VARFIMA(p,d,q) model is explored.This part analysises the model's statistical structure and its likelihood function, constructs the parameters'prior distribution and makes an inference for the parameters'conditional posterior distribution.; then through a series simulated by the EXCEL, implementes Bayesian VARFIMA(p,d,q) model simulation with WinBUGS.