Partially adaptive estimation of nonlinear models via a normal mixture
将线性模型的部分自适应方法扩展到非线性模型，在一般条件下建立渐近结果，并通过蒙特卡洛模拟表明新估计量在非正态分布下比非线性最小二乘估计更有效。
This paper extends the partially adaptive method Phillips (1994) provided for linear models to nonlinear models. Asymptotic results are established under conditions general enough they cover both cross-sectional and time series applications. The sampling efficiency of the new estimator is illustrated in a small Monte Carlo study in which the parameters of an autoregressive moving average are estimated. The study indicates that, for non-normal distributions, the new estimator improves on the nonlinear least squares estimator in terms of efficiency.