considered for the modelling of economic time series. The focus of this paper is on the simultaneous estimation of parameters … related to the stochastic processes of the mean part and the variance part of the model. The estimation method is based on … carried out to investigate the small-sample properties of the estimation procedure. We present two illustrations which are …

volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algorithms for this type of model are …

We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the … conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the … conditional variance is modelled by a stochastic volatility process. We develop a Monte Carlo maximum likelihood method to obtain …

The Bayesian Paradigm 7 2.1 Complete Models 10 2.2 Model …

Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility … models. While variants of the GARCH model are applied in scores of articles, use of the stochastic volatility model is less …

Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility … unobserved stochastic volatility, and the varying approaches that have been taken for such estimation. In order to simplify the … comprehension of these estimation methods, the main methods for estimating stochastic volatility are discussed, with focus on their …

semi-Markov structure designed to accommodate many of the stylized facts of credit rating migrations. Parameter estimation … experiment is carried out to show the effectiveness of the estimation procedure. An empirical application is presented for …