Efficient Estimation in Smooth Threshold Autoregressive (1) Models

Verifiable conditions are given for the existence of efficient estimation in Smooth Threshold Autoregressive models of order 1. The paper establishes local asymptotic normality in the semi-parametric setting which is then used to discuss adaptive and efficient estimates of the models. It is found that the adaptation is satisfied if the error densities are symmetric. Simulation results are presented to compare the conditional least squares estimate with the adaptive and efficient estimates for the models.