A Kalman filter for application to stationary or non-stationary time series is proposed. A major feature is a new initialisation method to accommodate non-stationary time series. The filter works on time series with missing values at any point of time including the initialisation phase. It can...

The principle that the simplest model capable of describing observed phenomena should also correspond to the best description has long been a guiding rule of inference. In this paper a Bayesian approach to formally implementing this principle is employed to develop model selection criteria for...

This paper investigates the time caseworkers spend supporting long-term foster care and adoption placements. Undertaken in Australia through collaboration between university and non-government agency researchers, the 'Cost of Support Study' tracked the hours that caseworkers spent supporting...

This paper investigates the time caseworkers spend supporting long-term foster care and adoption placements. Undertaken in Australia through collaboration between university and nongovernment agency researchers, the 'Cost of Support Study' tracked the hours that caseworkers spent supporting...

A general Bayesian Markov Chain Monte Carlo methodology is utilized for conducting an analysis of the intensity process of stock market data. The sampling scheme employed is a hybrid of the Gibbs and Metropolis Hastings algorithms. Both duration and count data time series approaches are utilized...

On any given night in Victoria, around 4,000 children and young people live under the care and protection of the State. For many young people, this care extends over a long period of time, sometimes until their 18th birthday. It is well documented that young people leaving State care often lack...

Organizations with large-scale inventory systems typically have a large proportion of items for which demand is intermittent and low volume. We examine different approaches to forecasting for such products, paying particular attention to the need for inventory planning over a multi-period...

The Beveridge-Nelson vector innovations structural time series framework is a new formulation that decomposes a set of variables into their permanent and transitory components. The proposed framework is flexible, modelling inter-series relationships and common features in a simple manner. In...

A well known property of the Beveridge Nelson decomposition is that the innovations in the permanent and transitory components are perfectly correlated. We use a single source of error state space model to exploit this property and perform a Beveridge Nelson decomposition. The single source of...

In this paper, we consider the introduction of Markov-switching (MS) processes to both the permanent and transitory components of the Beveridge-Nelson (BN) decomposition. This new class of MS models within the context of BN decomposition provides an alternative framework in the study of business...