Testing for a unit root in time series regression

Clicks: 24
ID: 289116
1988
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #92 of 204 articles by views in jurnal biometrika dan kependudukan

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 204 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
This paper proposes new tests for detecting the presence of a unit root in quite general time series models. Our approach is nonparametric with respect to nuisance parameters and thereby allows for a very wide class of weakly dependent and possibly heterogeneously distributed data. The tests accommodate models with a fitted drift and a time trend so that they may be used to discriminate between unit root nonstationarity and stationarity about a deterministic trend. The limiting distributions of the statistics are obtained under both the unit root null and a sequence of local alternatives. The latter noncentral distribution theory yields local asymptotic power functions for the tests and facilitates comparisons with alternative procedures due to Dickey & Fuller. Simulations are reported on the performance of the new tests in finite samples.
Reference Key
openalex_W2097580026 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Peter C.B. Phillips, Pierre Perrón
Are you one of these authors? Claim your profile
Journal jurnal biometrika dan kependudukan
Year 1988
DOI
10.1093/biomet/75.2.335
URL
https://doi.org/10.1093/biomet/75.2.335
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.