Nonparametric Decomposition of Time Series Data with Inputs

Abstract

The backfitting algorithm commonly used in estimating additive models is used to decompose the component shares explained by a set of predictors on a dependent variable in the presence of linear dependencies (multicollinearity) among the predictors. Simulated and actual data show that the backfitting methods are superior in terms of predictive ability as the degree of multicollinearity worsens. Furthermore, the additive smoothing splines are especially superior when the linear model yield inadequate fit to the data and the predictors exhibit extreme multicollinearity.