Nonlinear Correlograms and Partial Autocorrelograms

Heather Anderson and Farshid Vahid

No 19/03, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: 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.

Keywords: Nonlinear autocorrelograms; Nonlinear time series models; Neural networks; Model selection criteria; Nonlinear partial autocorrelograms (search for similar items in EconPapers)
JEL-codes: C22 C45 C51 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2003-11
New Economics Papers: this item is included in nep-ecm and nep-rmg
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Related works:
Journal Article: Nonlinear Correlograms and Partial Autocorrelograms* (2005)
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