Optimal tests in AR(m) time series model
Tewfik Lounis (LMNO)
TL;DR
This paper develops a method to evaluate estimation error in AR(m) models, ensuring tests retain their asymptotic power, with applications to AR(1), ARCH, and extended to AR(m) models.
Contribution
It introduces a new evaluation method for estimation error that preserves test power and extends asymptotic power analysis to AR(m) models.
Findings
Method preserves asymptotic power of tests.
Asymptotic power functions derived for AR(1) and ARCH.
Results extended to general AR(m) models.
Abstract
A method for an evaluation of the error between an unknown parameter and its estimator is developed. Its application enables us to preserve the asymptotic power of a constructed test. Testing problems in AR(1) and ARCH models are studied with a derivation of the asymptotic power function. Also the results are extended to AR(m) time series model.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsFault Detection and Control Systems · Advanced Statistical Methods and Models · Statistical Methods and Inference