Forecasting Australian Unemployment Rates

Cyclical asymmetry has been recognized as a non-linear phenomenon in recent studies examining unemployment rate time series. The probabilistic structure of such time series is different during economic upswings and downswings. So, with forecasting unemployment rates in mind, it seems intuitive that models should reflect this change in structure by incorporating non-linearities. This allows for the switching in optimizing behaviour between different phases of the business cycle. Accordingly, this paper evaluates the point forecasts from models of the monthly, aggregate Australian unemployment rate series; these models being drawn from both the linear and non-linear classes. The non-linear model is based on a standard logistic smooth transition autoregressive (LSTAR) model of unemployment which includes a lagged level term and a seasonal, rather than first-difference transition variable. Forecasts from this model are evaluated against the best-fitting linear autoregressive (AR) alternative. Dynamic point forecasts over twenty-four months, suggest that the LSTAR forecasts are more accurate than the linear AR alternative. However, there is no statistical difference between the forecasts from both models on a one-to-twelve step-ahead basis.