Asymptotic Moments of Autoregressive Estimates with a Near Unit Root and Minimax Risk

Abstract

This moments of the asymptotic distribution of the least-squares estimator of the local-to-unity autoregressive model are computed using computationally simple integration. These calculations show that conventional simulation estimation of moments can be substantially inaccurate unless thesimulationsamplesizeisverylarge. Wealsoexploretheminimaxefficiency of autoregressive coefficient estimation, and numerically show that a simple Stein shrinkage estimator has minimax risk which is uniformly better than least squares, even though the estimation dimension is just one