Article ID: | iaor19983124 |
Country: | United Kingdom |
Volume: | 29 |
Issue: | 3 |
Start Page Number: | 759 |
End Page Number: | 805 |
Publication Date: | Sep 1997 |
Journal: | Advances in Applied Probability |
Authors: | Feigin Paul D., Resnick Sidney I. |
Keywords: | programming: linear |
For autoregressive time series with positive innovations which either have heavy right or left tails, linear programming parameter estimates of the autoregressive coefficients have good rates of convergence. However, the asymptotic distribution of the estimators depends heavily on the distribution of the process and thus cannot be used for inference. A bootstrap procedure circumvents this difficulty. We verify the validity of the bootstrap and also give some general comments on the bootstrapping of heavy tailed phenomena.