This study deals with nonlinear dynamical economics and chaotic motion where a specific approach is taken to the evolution of prices in agricultural markets. It is shown that a nonlinear pertubation of the well established Cobweb Model can yield complex dynamic phenomena. Once the linearity assumption is given up the observed price fluctuations in commodity markets might be due to the much greater variety of possible dynamic outcomes than in the classical linear models. A nonlinear time series analysis is applied to search for empirical evidence of such endogenous nonlinearities. The book describes a selection of relatively new methods such as correlation integral diagnostics, testing for nonlinear dependencies in a time series, nearest neighbor prediction and uses a robust nonparametric methodology.
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