EBS Research — Current Projects

Estimating Pearson type IV distributions for high frequency financial time series

This project looks at the estimation of Pearson type IV distributions for high frequency financial time series using maximum likelihood and minimum quantile distant methods.

Key Researchers: Param Silvapulle

Exponential smoothing using a state-space framework

Researchers in the Business & Economics Forecasting Unit have been responsible for developing a statistical framework for the exponential smoothing methods of forecasting over the last few years. The framework facilitates the generation of logically sound prediction distributions. It also permits the use of information criteria for choosing between competing methods. Work is continuing on refinements to the approach.

Key Researchers: Ralph Snyder, Rob Hyndman, Muhammad Akram, Anne Koehler, Keith Ord, Simone Grose.

Environmental and epidemiological forecasting

Some respiratory diseases are impacted by environmental factors such as pollutant levels, pollen levels and meteorological conditions. In this project, we are developing some nonparametric models of the relationship between asthma hospital admissions and the various environmental covariates. This can then be used to predict hospital admissions a few days in advance which enables hospital staffing levels to be adjusted accordingly. A related task is to develop models of the covariates themselves so they can be forecast separately.

Key Researchers: Rob Hyndman and Bircan Erbas (University of Melbourne).

Forecasting in the frequency domain

This project looks at a frequency domain forecasting method and compares it to conventional time domain forecasting methods. The use of this virtually unknown frequency domain method is highlighted in amongst the vast, almost exclusive time domain forecasting literature of business, financial and economic applications.

Key Researchers: Ann Maharaj

Functional forecasting

Some data comes in the form of functions rather than individual observations. For example, mortality is a function of age, and it is of interest to forecast mortality in order to predict future population profiles. Another context for this type of data is in cancer monitoring where a person’s cancer status is predicted based on functional biometric data. In this project, we aim to develop new methods for predicting functional data.

Key Researchers: Rob Hyndman, Shahid Ullah, Don Poskitt, Arivulzaham Sengarapillai

Hierarchical forecasting

In this project we are forecasting large numbers of related time series which can be aggregated at several different levels. Important applications are to the Pharmaceutical Benefits Scheme and Labour Market forecasting. We aim to develop new statistical methodology for forecasting hierarchical time series which (1) provides point forecasts that are consistent across the levels of hierarchy; (2) allows for the correlations and interaction between the series at each level of the hierarchy; (3) provides estimates of forecast uncertainty which are consistent across the levels of hierarchy; and (4) is sufficiently flexible that ad hoc adjustments can be incorporated and important covariates can be included.

Key Researchers: Rob Hyndman, Chandra Shah, Roman Ahmed.

Model selection for automatic forecasting

When forecasting large numbers of items, it is important to have a fully automated procedure. This project is looking at automatic model selection for forecasting, in the context of exponential smoothing models and ARIMA models.

Key Researchers: Rob Hyndman, Ralph Snyder, Yeasmin Khandakar

Modelling consumer demand with very large numbers of commodities

This project investigates the problem of modelling very large consumer demand systems. Such systems are difficult to estimate because the number of free parameters increases very rapidly with the number of commodities.

Key Researchers: Keith McLaren

Modelling of illicit markets: The case of marijuana

Key Researchers: Xueyan Zhao and K. Clements*

Nonparametric approaches to nonlinear time series

This project investigates nonparametric and semiparametric approaches in nonlinear time series econometrics and financial econometrics.

Key Researchers: Max King, J. Gao* and D. Tjostheim*

Persistence in economic interpretation, measurement and inference

Key Researchers: Gael Martin and David Harris (Melbourne)

Time series of counts

Forecasting methods for time series data consisting of counts are being developed in this project. Such time series arise in many contexts including intermittent demand and stock transaction data. The aim is to extend the range of available models to allow covariates to be included linearly or non-parametrically and to provide a range of serial correlation patterns.

Key Researchers: Gael Martin, Ralph Snyder, Rob Hyndman, Lydia Shenstone, Chris Strickland.

Using option prices to conduct implicit Bayesian analysis of financial returns

Key Researchers: Gael Martin

Wavelet multiscale forecasting

In this project, a procedure to obtain forecasts for stationary time series and stationary long memory time series by means of wavelet multi-resolution analysis has been developed. Outcomes so far for stationary long memory processes are very encouraging. Work is continuing to further refine the procedure.

Key Researchers: Ann Maharaj and Don Percival (University of Washington)