Ebook: Nonlinear Dynamics in Economics: A Theoretical and Statistical Approach to Agricultural Markets

1. 1 Introduction In economics, one often observes time series that exhibit different patterns of qualitative behavior, both regular and irregular, symmetric and asymmetric. There exist two different perspectives to explain this kind of behavior within the framework of a dynamical model. The traditional belief is that the time evolution of the series can be explained by a linear dynamic model that is exogenously disturbed by a stochastic process. In that case, the observed irregular behavior is explained by the influence of external random shocks which do not necessarily have an economic reason. A more recent theory has evolved in economics that attributes the patterns of change in economic time series to an underlying nonlinear structure, which means that fluctua­ tions can as well be caused endogenously by the influence of market forces, preference relations, or technological progress. One of the main reasons why nonlinear dynamic models are so interesting to economists is that they are able to produce a great variety of possible dynamic outcomes - from regular predictable behavior to the most complex irregular behavior - rich enough to meet the economists' objectives of modeling. The traditional linear models can only capture a limited number of possi­ ble dynamic phenomena, which are basically convergence to an equilibrium point, steady oscillations, and unbounded divergence. In any case, for a lin­ ear system one can write down exactly the solutions to a set of differential or difference equations and classify them.

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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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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.
Content:
Front Matter....Pages I-IX
Introduction....Pages 1-32
A Nonlinear Cobweb Model....Pages 33-63
Are Time Series From Agricultural Markets Nonlinear? The Case of German Prices....Pages 64-121
A Nearest Neighbor Approach to Forecast Nonlinear Time Series....Pages 122-147
Conclusions and Outlook....Pages 148-156
Back Matter....Pages 157-158

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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.
Content:
Front Matter....Pages I-IX
Introduction....Pages 1-32
A Nonlinear Cobweb Model....Pages 33-63
Are Time Series From Agricultural Markets Nonlinear? The Case of German Prices....Pages 64-121
A Nearest Neighbor Approach to Forecast Nonlinear Time Series....Pages 122-147
Conclusions and Outlook....Pages 148-156
Back Matter....Pages 157-158
....