A Nonlinear Time Series Workshop | A Toolkit for Detecting and Identifying Nonlinear Serial Dependence

Douglas M. Patterson (u. a.)

ISBN 10: 1461346657 / ISBN 13: 9781461346654

Published by Springer, 2012

About this Item

A Nonlinear Time Series Workshop | A Toolkit for Detecting and Identifying Nonlinear Serial Dependence | Douglas M. Patterson (u. a.) | Taschenbuch | ix | Englisch | 2012 | Springer | EAN 9781461346654 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 105651762

Bibliographic Details

• Title: A Nonlinear Time Series Workshop | A Toolkit for Detecting and Identifying Nonlinear Serial Dependence
• Publisher: Springer
• Publication Date: 2012
• Language: English
• ISBN 10: 1461346657
• ISBN 13: 9781461346654
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 12D millimeters 235 millimeters width by 155 millimeters height by 12 millimeters depth
• Weight: 335 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

The analysis ofwhat might be called "dynamic nonlinearity" in time series has its roots in the pioneering work ofBrillinger (1965) - who first pointed out how the bispectrum and higher order polyspectra could, in principle, be used to test for nonlinear serial dependence - and in Subba Rao and Gabr (1980) and Hinich (1982) who each showed how Brillinger's insight could be translated into a statistical test. Hinich's test, because ittakes advantage ofthe large sample statisticalpropertiesofthe bispectral estimates became the first usable statistical test for nonlinear serial dependence. We are forever grateful to Mel Hinich for getting us involved at that time in this fascinating and fruitful endeavor. With help from Mel (sometimes as amentor,sometimes as acollaborator) we developed and applied this bispectral test in the ensuing period. The first application ofthe test was to daily stock returns {Hinich and Patterson (1982, 1985)} yielding the important discovery of substantial nonlinear serial dependence in returns, over and above the weak linear serial dependence that had been previously observed. The original manuscript met with resistance from finance journals, no doubt because finance academics were reluctant to recognize the importance of distinguishing between serial correlation and nonlinear serial dependence. In Ashley, Patterson and Hinich (1986) we examined the power and sizeofthe test in finite samples.