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Nonlinear Correlograms and Partial Autocorrelograms *
Author(s) -
Anderson Heather M.,
Vahid Farshid
Publication year - 2005
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/j.1468-0084.2005.00147.x
Subject(s) - predictability , nonlinear system , multivariate statistics , contrast (vision) , econometrics , simple (philosophy) , nonparametric statistics , mathematics , construct (python library) , artificial neural network , statistics , computer science , artificial intelligence , physics , philosophy , epistemology , quantum mechanics , programming language
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.