Download A Nonlinear Time Series Workshop: A Toolkit for Detecting by Douglas M. Patterson PDF

By Douglas M. Patterson

The advanced dynamic habit exhibited through many nonlinear platforms - chaos, episodic volatility bursts, stochastic regimes switching - has attracted a great deal of recognition lately. A Nonlinear Time sequence Workshop presents the reader with either the statistical heritage and the software program instruments beneficial for detecting nonlinear habit in time sequence information. the main helpful present detection suggestions are defined, together with Engle's LaGrange Multiplier attempt for conditional hetero-skedasticity and checks according to the correlation measurement and at the envisioned bispectrum. those suggestions are illustrated utilizing real info from fields akin to economics, finance, engineering, and geophysics.

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Additional info for A Nonlinear Time Series Workshop: A Toolkit for Detecting and Identifying Nonlinear Serial Dependence

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Ashley and Patterson 37 References Astrom, K. (1970) Introduction to Stochastic Control Theory Academic Press: New York . , Patterson, D. and M. Hinich (1986) "A Diagnostic Test for Nonlinear Dependence in Time Series Fitting Errors," Journal of Time Series Analysis Vol. 3, pp. 165-178 . Bollerslev, T. (1986) "Generalized Autoregr essive Conditional Heteroscedasticity," Journal of Econometrics 31, pp. 307·327 . Engle , R. (1982) "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of UK Inflation," Econometrica 50, pp.

11t can be expressed in terms of 2 DETECTING NONLINEAR SERIAL DEPENDENCE INTRODUCTION There are many statistical tests for nonlinear serial dependence in the literature. Some focus on a particular property characteristic ofnonlinear processes, such as conditional heteroskedasticity; some focus on a particular parametric family of models, as in the way the Tsay test considers quadratic models. Others, such as the Hinich tests, focus on particular moments. Still others, such as the BDS test, focus on more general measures of relatedness.

T 9. , etc. 11t can be expressed in terms of 2 DETECTING NONLINEAR SERIAL DEPENDENCE INTRODUCTION There are many statistical tests for nonlinear serial dependence in the literature. Some focus on a particular property characteristic ofnonlinear processes, such as conditional heteroskedasticity; some focus on a particular parametric family of models, as in the way the Tsay test considers quadratic models. Others, such as the Hinich tests, focus on particular moments. Still others, such as the BDS test, focus on more general measures of relatedness.

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