An Artificial Neural Network Test for Structural Change with Unspecified Parametric Form

Tests for a structural change of unknown timing in parameterized regression functions have been introduced previously under the maintained assumption that the models are correctly specified. However, the existing family of tests are unable to discriminate between structural change and misspecification. This paper introduces test statistics which do not require specification of the parametric form of the underlying data-generating process (DGP). I approximate it by a version of artificial neural networks (ANN). My simulation studies indicate that an ANN approximates the DGP quite well and that the derived tests have good power relative to the power envelope.