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...

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...

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 considers Beveridge-Nelson decomposition in a context where the permanent and transitory components both follow a Markov switching process. Our approach insorporates Markov switching into a single source of error state-space framework, allowing business cycle asymmetries and regime...

This paper is concerned with identifying an effective method for forecasting the lead time demand of slow-moving inventories. Particular emphasis is placed on prediction distributions instead of point predictions alone. It is also placed on methods which work with small samples as well as large...

This paper considers Beveridge-Nelson decomposition in a context where the permanent and transitory components both follow a Markov switching process. Our approach insorporates Markov switching into a single source of error state-space framework, allowing business cycle asymmetries and regime...