Some methods for assessing the need for non-linear models in business cycle analysis

Abstract

It is often suggested that non-linear models are needed to capture business cycle features. In this paper, we subject this view to some critical analysis. We examine two types of non-linear models designed to capture the bounce-back effect in US expansions. This means that these non-linear models produce an improved explanation of the shape of expansions over that provided by linear models. But this is at the expense of making expansions last much longer than they do in reality. Interestingly, the fitted models seem to be influenced by a single point in 1958 when a large negative growth rate in GDP was followed by good positive growth in the next quarter. This seems to have become embedded as a population characteristic and results in overly long and strong expansions. That feature is likely to be a problem for forecasting if another large negative growth rate was observed.