Using proprietary Australian Tax Office (ATO) data, we document the size, asset allocation and expenses for a sample of 209,420 Australian self-managed superannuation funds (SMSFs) for the three years to June 2010. Two recent Government reviews have highlighted a lack of basic knowledge of the...

Using proprietary data, this study examines auditor industry specialisation, professional brand effects and non-audit services (NAS) in the self-managed superannuation fund (SMSF) sector, the fastest growing and largest segment of the Australian $1.75 trillion retirement savings industry. We...

The bayesTFR package for R provides a set of functions to produce probabilistic projections of the total fertility rate (TFR) for all countries. In the model, a random walk with drift is used to project the TFR during the fertility transition, using a Bayesian hierarchical model to estimate the...

The number of self-managed superannuation funds (SMSFs) has grown at more than 5% p.a. over the past five years. The net growth rate consists of 7% new SMSFs and 2% closures. More than 570,000 SMSFs now serve over one million members but little is known about the experiences of members. We...

Self-managed superannuation funds (SMSFs) – small retirement savings funds with four or fewer members – now manage almost one third of retirement savings in Australia, and serve over one million members. The number of SMSFs has increased to more than half a million in two decades. Yet little...

Due to recent advances in methods and software for model-based clustering, and to the interpretability of the results, clustering procedures based on probability models are increasingly preferred over heuristic methods. The clustering process estimates a model for the data that allows for...

We develop a method to estimate and assess uncertainty in the total fertility rate over time. Our estimates are based on multiple imperfect observations from different data sources including surveys and censuses. We take account of measurement error by decomposing it into bias and variance and...