Monitoring processes with changing variances
John Ord,
Anne B. Koehler,
Ralph Snyder () and
Rob Hyndman
International Journal of Forecasting, 2009, vol. 25, issue 3, 518-525
Abstract:
Statistical process control (SPC) has evolved beyond its classical applications in manufacturing to monitoring economic and social phenomena. This extension has required the consideration of autocorrelated and possibly non-stationary time series. Less attention has been paid to the possibility that the variance of the process may also change over time. In this paper we use the innovations state space modeling framework to develop conditionally heteroscedastic models. We provide examples to show that the incorrect use of homoscedastic models may lead to erroneous decisions about the nature of the process.
Keywords: Control; charts; GARCH; Heteroscedasticity; Innovations; State; space; Statistical; process; control (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169-2070(09)00100-9
Full text for ScienceDirect subscribers only
Related works:
Working Paper: Monitoring Processes with Changing Variances (2008) 
Working Paper: Monitoring Processes with Changing Variances (2008) 
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:intfor:v:25:y:2009:i:3:p:518-525
Access Statistics for this article
International Journal of Forecasting is currently edited by R. J. Hyndman
More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().