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Nonlinear Time Series Analysis: Autoregressive Conditional Heteroskedasticity, Recurrence Quantification Analysis, Star Model

Nonlinear Time Series Analysis: Autoregressive Conditional Heteroskedasticity, Recurrence Quantification Analysis, Star Model

          
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About the Book

Chapters: Autoregressive Conditional Heteroskedasticity, Recurrence Quantification Analysis, Star Model, Phase Dispersion Minimization, Bispectrum, Nonlinear Autoregressive Exogenous Model, Trispectrum, Fnn Algorithm. Source: Wikipedia. Pages: 36. Not illustrated. Free updates online. Purchase includes a free trial membership in the publisher's book club where you can select from more than a million books without charge. Excerpt: In econometrics, a model featuring autoregressive conditional heteroskedasticity considers the variance of the current error term or innovation to be a function of the actual sizes of the previous time periods' error terms: often the variance is related to the squares of the previous innovations. Such models are often called ARCH models (Engle, 1982), although a variety of other acronyms is applied to particular structures of model which have a similar basis. ARCH models are employed commonly in modeling financial time series that exhibit time-varying volatility clustering, i.e. periods of swings followed by periods of relative calm. Specifically, let denote the error terms (return residuals, w.r.t. a mean process) and assume that, where and where the series are modeled by and where and . An ARCH(q) model can be estimated using ordinary least squares. A methodology to test for the lag length of ARCH errors using the Lagrange multiplier test was proposed by Engle (1982). This procedure is as follows: If an autoregressive moving average model (ARMA model) is assumed for the error variance, the model is a generalized autoregressive conditional heteroskedasticity (GARCH, Bollerslev(1986)) model. In that case, the GARCH(p, q) model (where p is the order of the GARCH terms and q is the order of the ARCH terms ) is given by Generally, when testing for heteroskedasticity in econometric models, the best test is the White test. However, when dealing with time series data, this means to test for ARCH errors (as des...http: //booksllc.net/?id=610752


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Product Details
  • ISBN-13: 9781156887929
  • Publisher: Books LLC
  • Publisher Imprint: Books LLC
  • Height: 152 mm
  • No of Pages: 38
  • Series Title: English
  • Weight: 68 gr
  • ISBN-10: 1156887925
  • Publisher Date: 14 Oct 2010
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 2 mm
  • Width: 229 mm

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