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By Michael Mccullough A, Thomas L. Marsh B and Ron C. Mittelhammer C


Phase space reconstruction is investigated as a diagnostic tool for determining the structure of detected nonlinear processes in regression residuals. Empirical evidence supporting this approach is provided using simulations from an Ikeda mapping and the S&P 500. Results in the form of phase portraits (e.g., scatter plots of reconstructed dynamical systems) provide qualitative information to discern structural components from apparent randomness and provide insights categorizing structural components into functional classes to enhance econometric/time series modeling efforts

Topics: Key Words, phase space reconstruction, mutual information, information and entropy
Year: 2011
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