Recurrence quantification analysis of business cycles

This chapter is dedicated to describe RQA applications in detecting spatio-temporal recurrent patterns of dynamical regimes of economic time series. Here we investigate the nature of economic dynamics and specifically of business cycles Orlando and Zimatore (Chaos, Solitons Fractals 110:82–94, 2018). Thus, after having devised a suitable model for business cycles such as that discussed in Chap. 16 and in References (Orlando, Math Comput Simul 125:83–98, 2016; 2018, https://doi.org/10.1007/978-3-319-71243-7_6), we look for an indicator that could show structural changes in a time series and that might be chaotic (Orlando and Zimatore, Indian Academy of Sciences Conference Series - Proceedings of the Conference on Perspectives in Nonlinear Dynamics - 2016, vol. 1, pp. 35–41, Springer, Berlin, 2017, https://doi.org/10.29195/iascs.01.01.0009). More specifically we apply RQA and statistical techniques to real time series to: (1) find common properties if and where they do exist, (2) discover some hidden features of economic dynamics and (3) highlight potential indicators of structural changes in the signal (i.e. precursors of a crash).