Monitoring Processes with Changing Variances
AbstractStatistical process control (SPC) has evolved beyond its classical applications in manufacturing to monitoring economic and social phenomena. This extension requires 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. The framework is extended to include counts data, when we also introduce a new type of chart, the P-value chart, to accommodate the changes in distributional form from one period to the next.
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Bibliographic InfoPaper provided by The George Washington University, Department of Economics, Research Program on Forecasting in its series Working Papers with number 2008-004.
Length: 26 pages
Date of creation: Jul 2008
Date of revision:
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control charts; count data; GARCH; heteroscedasticity; innovations; state space; statistical process control;
Other versions of this item:
- J. Keith Ord & Rob J. Hyndman & Anne B. Koehler & Ralph D. Snyder, 2008. "Monitoring Processes with Changing Variances," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics 4/08, Monash University, Department of Econometrics and Business Statistics.
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
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