EconPapers    
Economics at your fingertips  
 

Estimation and Testing for Unit Root Processes with GARCH (1, 1) Errors: Theory and Monte Carlo Evidence

Shiqing Ling (), W. K. Li and Michael McAleer

Econometric Reviews, 2003, vol. 22, issue 2, 179-202

Abstract: Least squares (LS) and maximum likelihood (ML) estimation are considered for unit root processes with GARCH (1, 1) errors. The asymptotic distributions of LS and ML estimators are derived under the condition α + β < 1. The former has the usual unit root distribution and the latter is a functional of a bivariate Brownian motion, as in Ling and Li [Ling, S., Li, W. K. (1998). Limiting distributions of maximum likelihood estimators for unstable autoregressive moving-average time series with GARCH errors. Ann. Statist.26:84-125]. Several unit root tests based on LS estimators, ML estimators, and mixing LS and ML estimators, are constructed. Simulation results show that tests based on mixing LS and ML estimators perform better than Dickey-Fuller tests which are based on LS estimators, and that tests based on the ML estimators perform better than the mixed estimators.

Keywords: Asymptotic distribution; Brownian motion; GARCH model; Least squares estimator; Maximum likelihood estimator; Unit root (search for similar items in EconPapers)
Date: 2003
References: Add references at CitEc
Citations: View citations in EconPapers (36)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1081/ETC-120020462 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Estimation and Testing for Unit Root Processes with GARCH (1, 1) Errors: Theory and Monte Carlo Evidence (2003) Downloads
Working Paper: Estimation and Testing for Unit Root Processes with GARCH(1,1) Errors: Theory and Monte Carlo Evidence (2001) 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:taf:emetrv:v:22:y:2003:i:2:p:179-202

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/LECR20

DOI: 10.1081/ETC-120020462

Access Statistics for this article

Econometric Reviews is currently edited by Dr. Essie Maasoumi

More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().

 
Page updated 2025-03-22
Handle: RePEc:taf:emetrv:v:22:y:2003:i:2:p:179-202