Working Papers 2003 – Abstracts
Nonlinear Correlograms and Partial Autocorrelograms
Heather M. Anderson and Farshid Vahid
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
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