Bootstrap prediction intervals for autoregressive time series
Clements, Michael P. and Kim, Jae H.. (2007) Bootstrap prediction intervals for autoregressive time series. Computational Statistics & Data Analysis, Vol.51 (No.7). pp. 3580-3594. ISSN 0167-9473Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.csda.2006.09.012
The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on the bootstrap is considered. Three methods are considered for countering the small-sample bias of least-squares estimation for processes which have roots close to the unit circle: a bootstrap bias-corrected OLS estimator; the use of the Roy-Fuller estimator in place of OLS; and the use of the Andrews-Chen estimator in place of OLS. All three methods of bias correction yield superior results to the bootstrap in the absence of bias correction. Of the three correction methods, the bootstrap prediction intervals based on the Roy-Fuller estimator are generally superior to the other two. The small-sample performance of bootstrap prediction intervals based on the Roy-Fuller estimator are investigated when the order of the AR model is unknown, and has to be determined using an information criterion. (c) 2006 Elsevier B.V. All rights reserved.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QA Mathematics
|Divisions:||Faculty of Social Sciences > Economics|
|Journal or Publication Title:||Computational Statistics & Data Analysis|
|Publisher:||Elsevier Science Ltd|
|Date:||1 April 2007|
|Number of Pages:||15|
|Page Range:||pp. 3580-3594|
|Access rights to Published version:||Restricted or Subscription Access|
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