Scrutinizing the capacity of unit root tests in detecting non-stationary time series
Clicks: 2
ID: 285844
2009
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.
Reader Engagement
Emerging Content
0.3
/100
2 views
1 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #3,423 of 3,757 articles by views in Malay Journal
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.
Mint this article as an NFT
Not yet mintedCreate 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
Abstract
Time series data such as asset prices, gross domestic product, and exchange rates almost always exhibit non-stationary in the mean. Hence, a mandatory econometric task before performing time series analysis is to determine whether the series is stationary to avoid spurious regression results. Non-stationary time series can be detected through various unit root testing procedures such as the Augmented Dickey-Fuller (ADF) and Phillips-Perron. As such, this study exposits unit root testing in detecting non-stationary and analyzes its respective capacity given the various statistical properties present. Results show that the ADF and PP unit root tests perform relatively the same with each other. However, the ADF test is more efficient in detecting that a random walk, random walk with drift, and random walk with drift along a deterministic trend is non-stationary while the PP test is more efficient in detecting stationary among the family of AR(1) data generating processes (DGPs). However, there are instances where the ADF and PP tests may not agree with each other. Thus, the properties of the DGP and the descriptive statistics of a series must be considered in deciding which unit root test to use and which unit root test must prevail. Unit root testing must be taken seriously in empirical research. The accurate detection of stationarity in time series is vital on the selection of the best DGP for accurate time series analyses.
| Reference Key |
persistent_1760656821_68f17db59dbe5
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Rivera, John Paolo R. |
| Journal | Malay Journal |
| Year | 2009 |
| DOI |
DOI not found
|
| URL | |
| 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.