ESTIMATION IN AN ADDITIVE MODEL WHEN THE COMPONENTS ARE LINKED PARAMETRICALLY
受非参数GARCH模型启发，研究分量参数化连接的非参数加性自回归模型，提出两步估计法，第一步用非参数平滑估计各分量，第二步利用参数约束估计参数，且第一步无需欠平滑，参数估计达到参数速率并具有正态极限。
Motivated by a nonparametric GARCH model we consider nonparametric additive autoregression models in the special case that the additive components are linked parametrically. We show that the parameter can be estimated with parametric rate and give the normal limit. Our procedure is based on two steps. In the first step nonparametric smoothers are used for the estimation of each additive component without taking into account the parametric link of the functions. In a second step the parameter is estimated by using the parametric restriction between the additive components. Interestingly, our method needs no undersmoothing in the first step.