TY - JOUR
T1 - Nonlinear correlograms and partial autocorrelograms
AU - Anderson, Heather M.
AU - Vahid, Farshid
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=29144475838&partnerID=8YFLogxK
U2 - 10.1111/j.1468-0084.2005.00147.x
DO - 10.1111/j.1468-0084.2005.00147.x
M3 - Article
SN - 0305-9049
VL - 67
SP - 957
EP - 982
JO - Oxford Bulletin of Economics and Statistics
JF - Oxford Bulletin of Economics and Statistics
IS - SUPPL.
ER -