The dichotomous characterization of the business cycle in recessions and expansions has been central in the literature over the last fifty years. However, there are various reasons to question the adequacy of this dichotomous approach for our understanding of business cycle dynamics, as well as for the prediction of future business cycle developments. In this context, the contribution of this paper to the literature is twofold. First, since a positive rate of growth in the level of economic activity can be considered as the normal scenario in modern economies due to both population and technological growth, it proposes a new non-parametric algorithm for the detection and dating of economic acceleration periods, trend or normal growth periods, and economic recessions. Second, it uses an ordered probit framework for estimating and forecasting these three business cycle phases for the German economy based on an automatized model selection approach using monthly macroeconomic and financial data . The empirical results show not only the empirical relevance of this new algorithm, but also significant asymmetries in the determinants of the different business cycle phases.
Orderes probits dynamic models real-time econometrics business cycle turning points