The impact of seasonal constants on forecasting seasonally cointegrated time series

In this paper we focus on the effect of (i) deleting, (ii) restricting or (iii) not restricting seasonal intercept terms on forecasting sets of seasonally cointegrated macroeconomic time series for Austria, Germany and the UK. A first empirical result is that the number of cointegrating vectors as well as the relevant estimated parameter values vary across the three models. A second result is that the quality of out-of-sample forecasts critically depends on the way seasonal constants are treated. In most cases, predictive performance can be improved by restricting the effects of seasonal constants. However, we find that the relative advantages and disadvantages of each of the three methods vary across the data sets and may depend on sample-specific features.