Download e-book for kindle: A Nonlinear Time Series Workshop: A Toolkit for Detecting by Douglas M. Patterson

By Douglas M. Patterson

The advanced dynamic habit exhibited by means of many nonlinear platforms - chaos, episodic volatility bursts, stochastic regimes switching - has attracted a great deal of cognizance in recent times. A Nonlinear Time sequence Workshop offers the reader with either the statistical heritage and the software program instruments worthy for detecting nonlinear habit in time sequence facts. the main beneficial latest detection suggestions are defined, together with Engle's LaGrange Multiplier try for conditional hetero-skedasticity and checks in response to the correlation measurement and at the expected bispectrum. those options are illustrated utilizing genuine facts from fields akin to economics, finance, engineering, and geophysics.

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

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D. (1991) A Test of Nonlinear Dynamics. Chaos ,and Instability: Theory and Evidence MIT Press: Cambridge. Brock, W. , and Scheinkman J. (1996) "A Test for Independence Based on the Correlation Dimension" Econometric Reviews 15, 197-235. Daile Molle, 1. W. and Hinich MJ. " Journal ofthe Acoustical Society ofAmerica 97, 2963-2978 . David, H. A. (1970) Order Statistics Wiley : New York. Engle, Robert F. (1982) "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation" Econometrica 50,987-1007.

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 . ) Each new sample is used to calculate a value for the test statistic under the null hypothesis of serial independence. The fraction of the NBOOT test statistics thus obtained which exceed the sample value of the test statistic from the original data is then reported as the significance level at which the null hypothesis can be rejected.

Granger, C. W. J. and Andersen, A. A. (1978) An Introduction to Bilinear Time Series Models Vandenhoeck and Ruprecht: Gottingen . Hinich, M, (1982) "Testing for Gaussianity and Linearity of a Stationary Time Series"Journal of Time Series Analys is 3,169-76. Hinich, M. (1996) "Testing for Dependence in the Input to a Linear Time Series Model" Journal of Nonparametric Statistics 6, 205-221 . Hinich, M. and Patterson D. M. (1985) "Evidence of Nonlinearity in Daily Stock Returns" Journal of Business and Economic Statistics 3, 69-77 .

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