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

By Douglas M. Patterson

The advanced dynamic habit exhibited by means of many nonlinear structures - 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 historical past and the software program instruments precious for detecting nonlinear habit in time sequence facts. the main important latest detection innovations are defined, together with Engle's LaGrange Multiplier try out for conditional hetero-skedasticity and checks in response to the correlation size and at the anticipated bispectrum. those ideas are illustrated utilizing genuine info from fields resembling 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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1985 , p. 246) . See Ramsey, Sayers and Rothman (1990) . The actual size of the test for N = 200 and 500 is examined in Chapter 4. The value of K is user-selectable in the Toolkit ; this parameter is set to 5 in the simulations reported below . See Brillinger and Rosenblatt (1967) for a rigorous treatment of the bispectrum. See Hinich (1982) and Ashley, Patterson , and Hinich (1986) for details. Based on simulati on results in the latter paper , M is set to the integer closest to N " in the calculati ons reported below .

4000 bootstrap : GAUSSIANITY IQR FRACTILE lOR FRACTILE 80% FRACTILE SIG. 345 SIG. LEVEL = OJ25 SIG. LEVEL = OJ3 I SIG. 269 asymptotic theory : GAUSSIANITY SIG. 0 I7 IQR FRACTILE SIG. 302 lOR FRACTILE SIG. 161 MCLEOD-L1 TEST bootstrap : USING UP TO USING UP TO USING UP TO USING UP TO USING UP TO USING UP TO USING UP TO USING UP TO USING UP TO :LAG 1 :LAG 2 :LAG 3 :LAG 4 :LAG 8 :LAG 12 :LAG 16 :LAG 20 :LAG 24 SIG. 400 SIG. 228 SIG. 323 SIG. 418 SIG. 191 SIG. 119 SIG. 255 SIG. 292 SIG. 218 asymptotic theory : USING UP TO USING UP TO USING UP TO USING UP TO USING UP TO USING UP TO USING UP TO :LAG :LAG :LAG :LAG :LAG :LAG :LAG I 2 3 4 8 12 16 SIG.

T E T} will be Gaussian. 99) is not intended to be an exhaustive definition of the linear model since, for example, a wide variety of point processes are also linear. 99) will be nonlinear if the input process n, alters the impulse response function h(t) . , up to and including t - L. 100) o where a(p, a) is another weighting function that is convolved with the input process. 100), x, will be nonlinear even if n, is Gaussian and independently distributed. The vast majority of nonlinear time series models are expressed as discrete time models.

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