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Auswirkungen des neuen Saisonbereinigungsverfahrens Census X-12-ARIMA auf die aktuelle Wirtschaftsanalyse in Deutschland

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  • Kirchner, Robert

Abstract

Das vorliegende Diskussionspapier untersucht methodische Änderungen der Saisonbereinigung, die mit dem Übergang von Census X-ll zu X-12-ARIMA verbunden sind, und ihre Konsequenzen für die Analyse der aktuellen Wirtschaftsentwicklung, wie sie in der Deutschen Bundesbank vorgenommen wird. Die Arbeit gliedert sich in drei Hauptkapitel. Im ersten Kapitel wird die Basis des Vergleichs beschrieben, nämlich das im Jahr 1965 vom U.S. Bureau of the Census veröffentlichte Verfahren X-ll, welches in einer modifizierten Form auch von der Deutschen Bundesbank angewendet wird. Anschließend werden die wesentlichen methodischen Änderungen des neuen Census-Verfahrens X-12-ARIMA gegenüber X-ll dargelegt (Kapitel m. Die Auswirkungen des Übergangs von X-ll zu X-12-ARIMA auf die Schätzung saisonbereinigter Angaben am aktuellen Ende wichtiger wirtschafts statistischer Reihen werden in Kapitel m näherungsweise quantifiziert. Jedes dieser Kapitel enthält Ausführungen über die zentralen Aspekte der Saisonbereinigung: die Schätzung von Saison-und Kalendereinflüssen sowie die Behandlung von Extremwerten. Beispiele verdeutlichen die jeweiligen Probleme und ihre Lösungsansätze ...

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  • Kirchner, Robert, 1999. "Auswirkungen des neuen Saisonbereinigungsverfahrens Census X-12-ARIMA auf die aktuelle Wirtschaftsanalyse in Deutschland," Discussion Paper Series 1: Economic Studies 1999,07, Deutsche Bundesbank.
    • Handle: RePEc:zbw:bubdp1:199907
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    References listed on IDEAS

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    1. Pierce, David A., 1980. "Data revisions with moving average seasonal adjustment procedures," Journal of Econometrics, Elsevier, vol. 14(1), pages 95-114, September.
    2. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
    3. Arnold Zellner, 1978. "Seasonal Analysis of Economic Time Series," NBER Books, National Bureau of Economic Research, Inc, number zell78-1, June.
    4. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 169-177, April.
    5. Victor Gómez & Agustín Maravall, 1996. "Programs TRAMO and SEATS, Instruction for User (Beta Version: september 1996)," Working Papers 9628, Banco de España.
    6. John Kuiper, 1978. "A Survey and Comparative Analysis of Various Methods of Seasonal Adjustment," NBER Chapters, in: Seasonal Analysis of Economic Time Series, pages 59-94, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Marcus Scheiblecker, 2004. "The Working-Day Effect in the Austrian Economy," Austrian Economic Quarterly, WIFO, vol. 9(1), pages 14-23, February.
    2. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72, July.
    3. Marcus Scheiblecker, 2003. "Der Arbeitstagseffekt im vierteljährlichen Bruttoinlandsprodukt. Eine empirische Analyse anhand saisonaler Zeitreihenmodelle," WIFO Monatsberichte (monthly reports), WIFO, vol. 76(11), pages 829-839, November.
    4. Gericke, Pierre-Andre & Seidel, Gerald, 2014. "Saisonbereinigung," EconStor Research Reports 209406, ZBW - Leibniz Information Centre for Economics.
    5. Klaus Abberger & Wolfgang Nierhaus, 2009. "Months for Cyclical Dominance und ifo Geschäftsklima," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(07), pages 11-19, April.
    6. Wolfgang Nierhaus, 2007. "Vierteljährliche volkswirtschaftliche Gesamtrechnungen für Sachsen mit Hilfe temporaler Disaggregation," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 14(04), pages .24-36, August.
    7. Wolfgang Nierhaus, 2014. "Saisonbereinigung in der Konjunkturanalyse: Ein Fallbeispiel," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 67(21), pages 35-39, November.
    8. Wolfgang Nierhaus, 2007. "Vierteljährliche volkswirtschaftliche Gesamtrechnungen für Sachsen mit Hilfe temporaler Disaggregation," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 14(04), pages 24-36, 08.
    9. Arz, Stephanus, 2006. "A new mixed multiplicative-additive model for seasonal adjusment," Discussion Paper Series 1: Economic Studies 2006,47, Deutsche Bundesbank.

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