Filter-design and model-based analysis of trends and cycles in the presence of outliers and structural breaks.

A large proportion of today’s econometric literature addresses the question of whether long run GDP follows a random walk or a log linear trend with one or more structural breaks. Much less attention is paid to the modelling of the long run trend and cycle component. In most studies, the trend is simply eliminated by taking first differences of the log of the series without considering the implications of this kind of trend removal for growth and cycles. Filter design and model-based approaches are used here to assess the long run trend and the cyclical component of Chile’s per capita GDP from 1820 to 2007. Careful attention is paid to outliers and trend breaks and how they influence the appraisal of the components. We show that filter and model-based approaches give comparable results if the filter and model parameters are not chosen mechanically but tailor-made for the time series to be investigated.