In this paper, time series
models and neural networks are used for prediction of unemployment in nine
Mediterranean countries. Accurate prediction of unemployment is very important
for economic policy reasons. FARIMA is a suitable model when long memory exists
in a time series and has been applied successfully for predicting unemployment.
However, potential data characteristics such as heteroskedasticity,
non-normality and non linearity in unemployment time series may reduce the
effectiveness of the classical FARIMA model. Here, is made an attempt for the
improvement of forecasting accuracy, by applying models which take into account
data characteristics, such as FARIMA/GARCH which takes into account
heteroskedasticity. Furthermore, non-linearity of the data is better captured
by Neural Network models rather than the traditional time series models such as
FARIMA and for this reason Artificial Neural Networks with multilayer feed-forward
architecture are considered as predictors for unemployment. Non-normality is
present at many cases of unemployment data and to further improve the results
are considered FARIMA and FARIMA/GARCH models with student t distribution
errors. Finally, is proposed a model selection based on data characteristics.
We employ monthly seasonally adjusted data (source is Eurostat database) from 2008 M1 to 2016M10 to
train the data and we consider 1 step-ahead forecasts for the next 12 months,
i.e. until 2017 M10 to compare the performance of the models.
C01, C22, C53
FARIMA/GARCH, FARIMA, multilayer feedforward neural nets, 1 step-ahead
predictions, t-distribution of errors.