Partially adaptive estimation of nonlinear models via a normal mixture

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.