Abstract:
Univariate spectral analysis is used to model seasonally unadjusted quarterly
unemployment rate data for Australia, 1978(2) to 2002(3). Data are tested for
three categories: persons, males and females. Dynamic out-of-sample forecasts are
made for 8 quarters using spectral analysis models evaluated against ARIMA model
counterparts. It is found that the spectral analysis models achieve higher levels of
forecasting accuracy than ARIMA counterparts, including turning point forecast
accuracy. These resuIts emerge in spite of weaker in-sample explanatory power of
the spectral models against the ARIMA models. It is concluded the results suggest
that the spectral model is ultimately better attuned to the various cyclical forces of
the past unfolding into the future.
