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On Seasonal Cycles, Unit Roots, And Mean Shifts Author info | Abstract | Publisher info | Download info | Related research | Statistics Philip Hans Franses
Timothy J. Vogelsang
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The interpretation of seasonality in terms of economic behavior depends on the form of the econometric time-series model that allows for a description of seasonality. Popular models often assume either approximate deterministic seasonality (cf. Miron (1996)) or stochastic trend seasonality (cf. Hylleberg (1994)). Inference from an inappropriate model can be shown to be invalid. Since much graphical evidence clearly suggests that seasonal fluctuations are not constant over time, we investigate whether the finding of seasonal unit roots can be due to neglected mean shifts. We provide relevant asymptotic theory and critical values for various test statistics. For a set of real gross domestic product series we find that much evidence for seasonal unit roots tends to disappear when a shift in the seasonal means is allowed. When we incorporate deterministic mean shifts in the deterministic seasonality model, we find that qualitative results on the presence of the so-called seasonal cycle documented in, for example, Miron and Beaulieu (1996) are robust. © 2000 by the President and Fellows of Harvard College and the Massachusetts Institute of Technolog
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Article provided by MIT Press in its journal The Review of Economics and Statistics .
Volume (Year): 80 (1998)
Issue (Month): 2 (May)
Pages: 231-240
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Handle: RePEc:tpr:restat:v:80:y:1998:i:2:p:231-240Contact details of provider: Web page: http://mitpress.mit.edu/journals/
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P.H. Franses & D. Van Dijk, 2001.
"The Forecasting Performance of Various Models for Seasonality and Nonlinearity for Quarterly Industrial Production ,"
Econometric Institute Report
222, Erasmus University Rotterdam, Econometric Institute.
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"The effects of institutional and technological change and business cycle fluctuations on seasonal patterns in quarterly industrial production series ,"
Working Paper Series in Economics and Finance
0429, Stockholm School of Economics, revised 16 May 2002.
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D. Van Dijk & D. Strikholm & T. Terasvirta, 2001.
"The effects of institutional and technological change and business cycle fluctiations on seasonal patterns in quarterly industrial production series ,"
Econometric Institute Report
220, Erasmus University Rotterdam, Econometric Institute.
[Downloadable!] Dijk, D.J.C. van & Strikholm, B. & Terasvirta, T., 2001.
"The effects of institutional and technological change and business cycle fluctiations on seasonal patterns in quarterly industrial production series ,"
Econometric Institute Report
EI 2001-12 Revision_Date:, Erasmus University Rotterdam, Econometric Institute.
[Downloadable!] Dick van Dijk 1 & Birgit Strikholm & Timo Teräsvirta, 2003.
"The effects of institutional and technological change and business cycle fluctuations on seasonal patterns in quarterly industrial production series ,"
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Artur C. B. da Silva Lopes & Antonio Montañés, 2004.
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Niel Haldrup & Antonio MontanŽs & Andreu Sanso, 2000.
"Measurement Errors and Outliers in Seasonal Unit Root Testing ,"
University of California at San Diego, Economics Working Paper Series
2000-15, Department of Economics, UC San Diego.
[Downloadable!] Haldrup, Niels & Montanes, Antonio & Sanso, Andreu, 2005.
"Measurement errors and outliers in seasonal unit root testing ,"
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Gabriel Pons Rotger, 2004.
"Seasonal Unit Root Testing Based on the Temporal Aggregation of Seasonal Cycles ,"
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Uwe Hassler & Paulo M. M. Rodrigues, 2002.
"Seasonal Unit Root Tests under Structural Breaks ,"
Darmstadt Discussion Papers in Economics
113, Institut für Volkswirtschaftslehre (Department of Economics), Technische Universität Darmstadt (Darmstadt University of Technology).
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Other versions: B. da Silva Lopes, Artur C., 2005.
"Finite sample effects of pure seasonal mean shifts on Dickey-Fuller tests ,"
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Harvey, David I. & Leybourne, Stephen J. & Newbold, Paul, 2002.
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Antonio Montanes & Andreu Sanso, 2001.
"The Dickey-Fuller Test Family and Changes in the Seasonal Pattern ,"
Annales d'Economie et de Statistique ,
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