Journal article
Estimation Bias in the First-Order Autoregressive Model and Its Impact on Predictions and Prediction Intervals
Communications in statistics. Simulation and computation, Vol.38(4), pp.771-787
04/2009
DOI: 10.1080/03610910802645354
Abstract
The least squares estimate of the autoregressive coefficient in the AR(1) model is known to be biased towards zero, especially for parameters close to the stationarity boundary. Several methods for correcting the autoregressive parameter estimate for the bias have been suggested. Using simulations, we study the bias and the mean square error of the least squares estimate and the bias-corrections proposed by Kendall and Quenouille.
We also study the mean square forecast error and the coverage of the 95% prediction interval when using the biased least squares estimate or one of its bias-corrected versions. We find that the estimation bias matters little for point forecasts, but that it affects the coverage of the prediction intervals. Prediction intervals for forecasts more than one step ahead, when calculated with the biased least squares estimate, are too narrow.
Details
- Title: Subtitle
- Estimation Bias in the First-Order Autoregressive Model and Its Impact on Predictions and Prediction Intervals
- Creators
- Johannes Ledolter - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Communications in statistics. Simulation and computation, Vol.38(4), pp.771-787
- Publisher
- Taylor & Francis Group
- DOI
- 10.1080/03610910802645354
- ISSN
- 0361-0918
- eISSN
- 1532-4141
- Language
- English
- Date published
- 04/2009
- Academic Unit
- Statistics and Actuarial Science; Business Analytics
- Record Identifier
- 9984380524902771
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