In this paper, we introduce randomized pivots for the means of short and long memory linear processes. We show that, under the same conditions, these pivots converge in distribution to the same limit as that of their classical non-randomized counterparts. We also present numerical results that indicate that these randomized pivots significantly outperform their classical counterparts and as a result they lead to a more accurate inference about the population mean.

Additional Metadata
Keywords Central limit theorem, Randomized pivots, Short and long memory time-series
Persistent URL dx.doi.org/10.3150/16-BEJ819
Journal Bernoulli
Citation
Csörgo, M, Nasari, M.M. (Masoud M.), & Ould Haye, M. (2017). Randomized pivots for means of short and long memory linear processes. Bernoulli, 23(4A), 2558–2586. doi:10.3150/16-BEJ819