Phase Space Reconstruction from Economic Time Series Data: Improving Models of Complex Real-World Dynamic Systems

Abstract

Failure of economic models to anticipate the global financial crisis illustrates the need for modeling to better capture complex real-world dynamics. Conventional models—in which economic variables evolve toward equilibria or fluctuate about equilibria in response to exogenous random shocks—are ill-equipped to portray complex real-world dynamics in which economic variables may cycle aperiodically along low-dimensional ‘strange attractors’. We present a method developed in the physics literature—‘phase space reconstruction’—that reconstructs strange attractors present in real-world dynamical systems using time series data on a single variable. Phase space reconstruction provides pictures of real-world dynamics that can guide model specificationphase space reconstruction, time series data, economic dynamics, Agribusiness, Agricultural and Food Policy, Food Consumption/Nutrition/Food Safety, Food Security and Poverty, Production Economics, Risk and Uncertainty,