Nonlinear Correlograms and Partial Autocorrelograms*
提出基于神经网络的条件均值可预测性度量，并构建非线性相关图和偏相关图，计算简单且在小样本中表现良好。
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.