Robust confidence intervals for autoregressive coefficients near one
Samuel Brodsky Thompson, Harvard University

Abstract

We construct outlier robust confidence sets for autoregressive roots near unity. There are a few difficulties in doing this - the asymptotics for robust methods generally involve several poorly estimated nuisance parameters, and robust procedures are more difficult to compute than least squares based methods. We propose a family of "aligned" robust procedures that eliminate the need to estimate some of the nuisance parameters. The procedures are computationally no more burdensome than least squares. With thick-tailed data the robust sets outperform those based on normality.