Nonlinear Correlograms and Partial Autocorrelograms
journal contributionposted on 07.06.2017 by Anderson, Heather M., Vahid, Farshid
Any type of content formally published in an academic journal, usually following a peer-review process.
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.