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

<p align="left">&nbsp;</p><p><span style="font-size: xx-small;">Failure of economic models to anticipate the global financial crisis illustrates the need for modeling to better capture complex real-world dynamics. Conventional models&mdash;in which economic variables evolve toward equilibria or fluctuate about equilibria in response to exogenous random shocks&mdash;are ill-equipped to portray complex real-world dynamics in which economic variables may cycle aperiodically along low-dimensional &lsquo;strange attractors&rsquo;. We present a method developed in the physics literature&mdash;&lsquo;phase space reconstruction&rsquo;&mdash;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 specification. </span>