Jackknife estimation with a unit root
We study jackknife estimators in a first-order autoregression with a unit root. Non-overlapping sub-sample estimators have different limit distributions, so the jackknife does not fully eliminate first-order bias. We therefore derive explicit limit distributions of the numerator and denominator to c...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
2013-07.
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| Subjects: | |
| Online Access: | Get fulltext |
| LEADER | 00825 am a22001333u 4500 | ||
|---|---|---|---|
| 001 | 351304 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Chambers, Marcus J. |e author |
| 700 | 1 | 0 | |a Kyriacou, Maria |e author |
| 245 | 0 | 0 | |a Jackknife estimation with a unit root |
| 260 | |c 2013-07. | ||
| 856 | |z Get fulltext |u https://eprints.soton.ac.uk/351304/1/S0167715213000990 | ||
| 520 | |a We study jackknife estimators in a first-order autoregression with a unit root. Non-overlapping sub-sample estimators have different limit distributions, so the jackknife does not fully eliminate first-order bias. We therefore derive explicit limit distributions of the numerator and denominator to calculate the expectations that determine optimal jackknife weights. Simulations show that the resulting jackknife estimator produces substantial reductions in bias and RMSE | ||
| 655 | 7 | |a Article | |