Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/307916 
Erscheinungsjahr: 
2022
Quellenangabe: 
[Journal:] AStA Advances in Statistical Analysis [ISSN:] 1863-818X [Volume:] 107 [Issue:] 4 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2022 [Pages:] 693-712
Verlag: 
Springer, Berlin, Heidelberg
Zusammenfassung: 
We consider a linear measurement error model (MEM) with AR(1) process in the state equation which is widely used in applied research. This MEM could be equivalently re-written as ARMA(1,1) process, where the MA(1) parameter is related to the variance of measurement errors. As the MA(1) parameter is of essential importance for these linear MEMs, it is of much relevance to provide instruments for online monitoring in order to detect its possible changes. In this paper we develop control charts for online detection of such changes, i.e., from AR(1) to ARMA(1,1) and vice versa, as soon as they occur. For this purpose, we elaborate on both cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts and investigate their performance in a Monte Carlo simulation study. The empirical illustration of our approach is conducted based on time series of daily realized volatilities.
Schlagwörter: 
Statistical process control
Measurement error
Control charts
Volatility modeling
JEL: 
C22
C44
C58
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