Investigating the structure of expansions and recessions in the US business cycle: a modified recursive partitioning approach

In this paper the problem of identifying the structure of expansions and recessions in the US economy is placed in the framework of recursive partitioning and discriminant analysis. The classification provided by the National Bureau of Economic Research (NBER) is considered. Using as covariates the main variables and indicators deemed useful to predict the business cycle, a modified recursive partitioning approach is proposed; at each step (tree node) the method identifies the linear combination of the covariates that discriminates the most between being in and out of a recession; this new covariate is then used to split the data. The application to the case of the US business cycle and the comparison with classical logistic regression shows the merits of the proposed approach that represents a useful to tool to identify and to interpret the structure of expansions and recessions.