The purpose of this article is to describe how inflation analysis and forecasting has been carried out in the Bank, with particular emphasis on recent research and the new challenges facing the Bank following the launch of the euro on 1 January 1999. Broadly speaking the approach adopted by the Bank over a number of years has been an eclectic one which combines judgement and a range of formal a...

This paper poses the problem of model order determination of an autoregressive (AR) pro ess within a Bayesian framework. Several original hierar hi al prior models are proposed that allow for the stability of the model to be enfor ed and a ount for a possible unknown initial state. Obtaining the posterior model order probabilities requires integration of the resulting posterior distribution, an...

In this paper a Bayesian approach to unit root testing for panel data models is proposed based on the comparison of stationary autoregressive models with and without individual deterministic trends, with their counterpart models with a unit autoregressive root. This is done under cross-sectional dependence among the units of the panel. Simulation experiments are conducted with the aim to assess...

A variational Bayesian autoregressive conditional heteroskedastic (VB-ARCH) model is presented. The ARCH class of models is one of the most popular for economic time series modeling. It assumes that the variance of the time series is an autoregressive process. The variational Bayesian approach results in an approximation to the full posterior distribution over ARCH model parameters, and provide...

The aim of this study is to diagnose epileptic seizures by using different machine learning algorithms. For this purpose, the frequency components of the EEG are extracted by using the discrete wavelet transform (DWT) and parametric methods based on autoregressive (AR) model. Both these two feature extraction methods are applied to the input of machine learning classification algorithms such as...