Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/101948 
Erscheinungsjahr: 
2014
Schriftenreihe/Nr.: 
FZID Discussion Paper No. 90-2014
Verlag: 
Universität Hohenheim, Forschungszentrum Innovation und Dienstleistung (FZID), Stuttgart
Zusammenfassung: 
Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has at- tracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The general-to-specific approach to the detection of structural change, currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unit-root autoregressions. By focusing on impulse-and step-indicator saturation, we investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality and a stationary component. Further, we apply both kinds of indicator saturation to detect additive outliers and level shifts in the industrial production series in five European countries.
Schlagwörter: 
indicator saturation
seasonal adjustment
structural time series model
outliers
structural change
general-to-specific approach
state space model
JEL: 
C22
C51
C53
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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