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.