Robust inference in semiparametric spatial-temporal models

Abstract

A semi-parametric spatial-temporal model is estimated using a hybrid of forward search algorithm and nonparametric regression in the context of backfitting an additive model. The model can account for a structural change represented by a function of time. Robust estimates of the parametric component are produced and the predicted model also exhibit good predictive ability. Nonparametric bootstrap is used to facilitate significance test for structural change even without prior knowledge of its nature. The test is properly-sized and powerful as observed from the simulation studies.