EconPapers    
Economics at your fingertips  
 

Spectral approach to parameter-free unit root testing

Natalia Bailey () and Liudas Giraitis

Computational Statistics & Data Analysis, 2016, vol. 100, issue C, 4-16

Abstract: A relatively simple frequency-type testing procedure for unit root potentially contaminated by an additive stationary noise is introduced, which encompasses general settings and allows for linear trends. The proposed test for unit root versus stationarity is based on a finite number of periodograms computed at low Fourier frequencies. It is not sensitive to the selection of tuning parameters defining the range of frequencies so long as they are in the vicinity of zero. The test does not require augmentation, has parameter-free non-standard asymptotic distribution and is correctly sized. The consistency rate under the alternative of stationarity reveals the relation between the power of the test and the long-run variance of the process. The finite sample performance of the test is explored in a Monte Carlo simulation study, and its empirical application suggests rejection of the unit root hypothesis for some of the Nelson–Plosser time series.

Keywords: Unit root test; Additive noise; Parameter-free distribution (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S016794731500119X
Full text for ScienceDirect subscribers only.

Related works:
Working Paper: Spectral Approach to Parameter-Free Unit Root Testing (2015) Downloads
Working Paper: Spectral Approach to Parameter-Free Unit Root Testing (2015) Downloads
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:eee:csdana:v:100:y:2016:i:c:p:4-16

DOI: 10.1016/j.csda.2015.05.002

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2021-09-25
Handle: RePEc:eee:csdana:v:100:y:2016:i:c:p:4-16