Patters D.M., Ashley R.A. A Nonlinear Time Series Workshop: A Toolkit for Detecting and Identifying Nonlinear Serial Dependence

New York: Springer, 2000. - 201p.The complex dynamic behavior exhibited by many nonlinear systems - chaos, episodic volatility bursts, stochastic regimes switching - has attracted a good deal of attention in recent years. A Nonlinear Time Series Workshop provides the reader with both the statistical background and the software tools necessary for detecting nonlinear behavior in time series data. The most useful existing detection techniques are described, including Engle's LaGrange Multiplier test for conditional hetero-skedasticity and tests based on the correlation dimension and on the estimated bispectrum. These techniques are illustrated using actual data from fields such as economics, finance, engineering, and geophysics.Nonlinearity in Stochastic Processes: What it is and Why it Matters
Detecting Nonlinear Serial Dependence
How to Run the Toolkit Program on a PC
Artificially Generated Data: Size Considerations
Artificially Generated Data: Power and Model Specification Considerations
Analysis of Stock Market Returns
Glint Tracking Errors in Radar
Seismic Data
Analysis of U.S. Real GNP
Dynamic Structure of Macroeconomic Technology Shocks
Climatological Data