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

By Douglas M. Patterson

ISBN-10: 144198688X

ISBN-13: 9781441986887

ISBN-10: 1461346657

ISBN-13: 9781461346654

The complicated dynamic habit exhibited by way of many nonlinear structures - chaos, episodic volatility bursts, stochastic regimes switching - has attracted a great deal of consciousness in recent times. A Nonlinear Time sequence Workshop presents the reader with either the statistical historical past and the software program instruments worthwhile for detecting nonlinear habit in time sequence information. the main beneficial current detection recommendations are defined, together with Engle's LaGrange Multiplier try for conditional hetero-skedasticity and assessments in accordance with the correlation measurement and at the expected bispectrum. those options are illustrated utilizing genuine information 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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A typical value would be 5; the maximum allowed value is 50. Then enter 4 zeroes, each separated by at least one space, and 4 zeroes with decimal points. line II: Same as line 10 only for the Tsay test; the maximumnumber of lags allowed is 12. 347 B1COVARIANCETEST bootstrap: USING UP TO LAG: 7 SIG. 017 asymptotic theory: USING UP TO LAG: 7 SIG.

O(£\t) 8. By o(£\t) we mean that ---0 as £\t-O. £\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.

Figure 3-3 displays the last of the prewhitening models estimated and the actual selected prewhitening model. In the Figure , an AR(l) model was chosen because it minimized the Schwartz criterion for model selection. Next the output file lists results on each test. The results labeled, "asymptotic theory" are based on the large sample distributions of the relevant test statistics, discussed in Chapter 2. For the bootstrap results, NBOOT "new" samples were independently drawn from the empirical distr ibution of the pre-whitened data .

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A Nonlinear Time Series Workshop: A Toolkit for Detecting and Identifying Nonlinear Serial Dependence by Douglas M. Patterson


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