Bayesian Estimation Of Parameters Of Conditional Heteroscedasticity Time-varying Autoregressive Models

This paper mainly studied the conditional heteroscedasticity bayesian estimation of the time-varying autoregressive model,the time-varying autoregressive model parameters based on bayesian estimation and variance of the disturbance to obey the ARCH(1)model,the parameters of the model is not obey random walk process,such as restrictions,but assumes that different times its successively state has certain correlation coefficient,and the shorter the time interval distance its correlation performance is more intense.As a result of the time the unrepeatable,usually we can only get a single sample,in this case using bayesian esti-mation method is used to estimate the parameters.Parameters are obtained by the bayesian formula,the posterior distribution,with the posterior mean estimate parameters;For com-plex posterior distribution to compute the conditional distribution,if the conditions are not known distribution,are not the direct sampling,each component of Metropolis-Hastings sampling method to sample,the sample mean estimate parameters,the bayesian estimation of parameters is obtained.Based on the analysis of a simple conditional heteroscedasticity time-varying model,carry on the numerical simulation to show the model coefficient esti-mates of the effect.On China's GDP growth rate of example analysis show that estimates of the model parameters is obtained by bayesian estimation method,is able to reveal the inherent law of practical problems exist in the process of change.