A finite mixture analysis of structural breaks in the G-7 gross domestic product series

In this paper we apply a clustering procedure to detect trend changes in macroeconomic data, focusing on the GDP time series for the G-7 countries. A finite mixture of regression models is considered to show different patterns and changes in GDP slopes over time in the long-trend component. Two popular trend-cycle decompositions (i.e., Beveridge and Nelson Decomposition and Hodrick and Prescott filter) are considered in a preliminary step of the analysis to stress the differences between the two methods in terms of the inferred clustering, if any. This approach can be used also to detect structural breaks or change points and it is an alternative to existing approaches in a probabilistic framework. We also discuss international changes in the GDP distribution for the G-7 countries, highlighting similarities, e.g., in break dates, aiming at adding more insights on the economic integration among countries. Our findings suggest that by looking at changes in slope over time a mixture of regression models is able to detect change points, also compared with alternative procedures.