Abstract :
Additive coefficient model (Xue and Yang, 2006a, 2006b) is a flexible regression
and autoregression tool that circumvents the “curse of dimensionality.” We propose
spline-backfitted kernel (SBK) and spline-backfitted local linear (SBLL) estimators
for the component functions in the additive coefficient model that are both (i) computationally
expedient so they are usable for analyzing high dimensional data, and
(ii) theoretically reliable so inference can be made on the component functions with
confidence. In addition, they are (iii) intuitively appealing and easy to use for practitioners.
The SBLL procedure is applied to a varying coefficient extension of the
Cobb-Douglas model for the U.S. GDP that allows nonneutral effects of the R&D
on capital and labor as well as in total factor productivity (TFP).
