Nonlinear Correlograms and Partial Autocorrelograms

This paper proposes neural network based measures of predictability in conditional mean, and then uses them to construct nonlinear analogues to autocorrelograms and partial autocorrelograms. In contrast to other measures of nonlinear dependence that rely on nonparametric estimation of densities or multivariate integration, our autocorrelograms are simple to calculate and appear to work well in relatively small samples.

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Year of publication:	2003-11
Authors:	Anderson, Heather M. ; Vahid, Farshid
Institutions:	Department of Econometrics and Business Statistics, Monash Business School
Subject:	Nonlinear autocorrelograms \| Nonlinear time series models \| Neural networks \| Model selection criteria \| Nonlinear partial autocorrelograms

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Extent:	application/pdf
Series:	Monash Econometrics and Business Statistics Working Papers.
Type of publication:	Book / Working Paper
Language:	English
Notes:	Number 19/03 29 pages
Classification:	C22 - Time-Series Models ; C45 - Neural Networks and Related Topics ; C51 - Model Construction and Estimation
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005087615