Nonlinear Time Series Workshop

Name: Nonlinear Time Series Workshop
Brand: Springer-Verlag New York Inc.
SKU: 9781461346654
Price: 57970 HUF
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A Toolkit for Detecting and Identifying Nonlinear Serial Dependence

Szerző: Douglas M. Patterson, Richard A. Ashley

Nyelv:

Kötés: Puha kötésű

Kiadó: Springer-Verlag New York Inc.

Elérhetőség: Beszállítói készleten

Küldés 5-8 napon belül

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The analysis ofwhat might be called "dynamic nonlinearity" in time series has its roots in the pione...

Információk a könyvről

Szerző

Douglas M. Patterson, Richard A. Ashley

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2012

oldal

201

EAN

9781461346654

ISBN

1461346657

Enbook ID

05257697

Kiadó

Springer-Verlag New York Inc.

Súly

338

Méretek

155 x 235 x 12

Teljes leírás

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.

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Nonlinear Time Series Workshop

A Toolkit for Detecting and Identifying Nonlinear Serial Dependence

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