Additive Nonlinear ARX Time Series and Projection Estimates
提出用投影方法识别和估计加性非线性ARX模型中的内生和外生分量，给出核估计的渐近正态性理论，适用于时间序列分析。
We propose projections as means of identifying and estimating the components (endogenous and exogenous) of an additive nonlinear ARX model. The estimates are nonparametric in nature and involve averaging of kernel-type estimates. Such estimates have recently been treated informally in a univariate time series situation. Here we extend the scope to nonlinear ARX models and present a rigorous theory, including the derivation of asymptotic normality for the projection estimates under a precise set of regularity conditions.