RQA correlations on real business cycles time series

Abstract

Trade cycles are complex phenomena which oscillate because of economic downturns and expansions. Recurrence quantification analysis (RQA) detects state changes without necessitating any a priori mathematical assumption and highlights hidden features of the dynamics both at equilibrium and near transition phases. This paper aims to understand some potential application of recurrence quantification analysis in detecting recessions.

Identification of the nature of dynamical systems with recurrence plots and convolution neural networks: A preliminary test

“…Thus, this research could provide the link between economic theory and identification of real dynamics based on machine learning classifiers and RQA/RP feature extraction where the latter has been successfully used to discover hidden dynamics and structural changes in economics [9,10].…”

“…where is a threshold distance [2,1]. This concept has been widely applied in various fields such as astrophysics [3,4,5], damage detection in engineering [6], molecular dynamics [7,8], economics [9,10,11], and medicine [12,13].…”

On risk and market sentiments driving financial share price dynamics

“…Moreover, they suggest a deterministic model which "performs at least as well as one of the best stochastic models" while offering "additional insight into the essential mechanisms that drive financial markets". Thus, theoretically, extreme events can be modelled using determin-istic models [18], and empirically, determinism can be observed in real data [19,20].…”

Resilience and Complex Dynamics — Safeguarding Local Stability Against Global Instability

“…Source (Orlando and Zimatore 2020a) dynamics share characteristics that make the proposed model a suitable tool to simulate economic reality Orlando and Zimatore (2020b). In particular, in the case of RQA, it has been demonstrated that it can be used for the early detection of recessions and that it can distinguish between stock and flow macroeconomic variables as well as between real and nominal data Orlando and Zimatore (2017;2018b;a). The above methodology is useful for discovering the underlying dynamics of economic time series, especially where other methods may fail due to the randomness, nonlinearity and non-stationarity of the data.…”