Jackknife Bias Reduction in the Presence of a Near-Unit Root
AbstractThis paper considers the specification and performance of jackknife estimators of the autoregressive coefficient in a model with a near-unit root. The limit distributions of sub-sample estimators that are used in the construction of the jackknife estimator are derived, and the joint moment generating function (MGF) of two components of these distributions is obtained and its properties explored. The MGF can be used to derive the weights for an optimal jackknife estimator that removes fully the first-order finite sample bias from the estimator. The resulting jackknife estimator is shown to perform well in finite samples and, with a suitable choice of the number of sub-samples, is shown to reduce the overall finite sample root mean squared error, as well as bias. However, the optimal jackknife weights rely on knowledge of the near-unit root parameter and a quantity that is related to the long-run variance of the disturbance process, which are typically unknown in practice, and so, this dependence is characterised fully and a discussion provided of the issues that arise in practice in the most general settings. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Chambers, M.J.; Kyriacou, M. Jackknife Bias Reduction in the Presence of a Near-Unit Root. Econometrics 2018, 6, 11.
Chambers MJ, Kyriacou M. Jackknife Bias Reduction in the Presence of a Near-Unit Root. Econometrics. 2018; 6(1):11.Chicago/Turabian Style
Chambers, Marcus J.; Kyriacou, Maria. 2018. "Jackknife Bias Reduction in the Presence of a Near-Unit Root." Econometrics 6, no. 1: 11.