Combination of forecasts with an application to unemployment rate

Combining forecast values based on simple univariate models may produce more favourable results than complex models. in this study,the results of combining the forecast values of naïve model,single exponential smoothing model,the autoregressive moving average (arima) model,and holt method are shown to be superior to that of the error correction model (ecm).malaysia’s unemployment rates data are used in this study. the independent variable used in the ecm formulation is the industrial production index. both data sets were collected for the months of january 2004 to december 2010. the selection criteria used to determine the best model,is the mean square error (mse),root mean squared error (rmse) and mean absolute percentage error (mape). initial findings showed that both time series data sets were not influenced by the seasonality effect. © 2017 universiti putra malaysia press.