Estimating Smooth Transition Autoregressive Models with GARCH Errors in the Presence of Extreme Observations and Outliers
Felix Chan and
Michael McAleer
ISER Discussion Paper from Institute of Social and Economic Research, Osaka University
Abstract:
This paper investigates several empirical issues regarding quasimaximum likelihood estimation of Smooth Transition Autoregressive (STAR) models with GARCH errors, specifically STAR-GARCH and STAR-STGARCH. Convergence, the choice of different algorithms for maximising the likelihood function, and the sensitivity of the estimates to outliers and extreme observations, are examined using daily data for S&P 500, Heng Seng and Nikkei 225 for the period January 1986 to April 2000.
Date: 2001-05
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://www.iser.osaka-u.ac.jp/library/dp/2001/dp0539.pdf
Related works:
Journal Article: Estimating smooth transition autoregressive models with GARCH errors in the presence of extreme observations and outliers (2003) 
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:dpr:wpaper:0539
Access Statistics for this paper
More papers in ISER Discussion Paper from Institute of Social and Economic Research, Osaka University Contact information at EDIRC.
Bibliographic data for series maintained by Librarian ().