Nonparametric panel stationarity testing with an application to crude oil production

María José Presno, Manuel Landajo and Paula Fernandez-Gonzalez

Journal of Applied Statistics, 2022, vol. 49, issue 4, 1033-1048

Abstract: A nonparametric panel stationarity test is proposed which offers the advantage of not requiring prior specification of the trend function for each of the series in the panel. A bootstrap implementation of the test is outlined and its finite sample performance is analyzed via Monte Carlo simulations. An application is also included where the proposed test is used to analyze the stochastic properties of monthly crude oil production for a panel of 20 -both OPEC and non-OPEC- countries from 1973 to 2015. Our analysis detects strong evidence of non-stationarity, both globally and group-wise. Results have implications for the effectiveness of government intervention and stabilization policies.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2020.1846691 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:49:y:2022:i:4:p:1033-1048

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2020.1846691

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().