IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/58033.html
   My bibliography  Save this paper

Identifying the Sources of Seasonal Effects in an indirectly adjusted Chain-Linked Aggregate: A Framework for the Annual Overlap Method

Author

Listed:
  • Cobb, Marcus

Abstract

The use of chain-linked methods reduces significantly the problem of price structure obsolescence present in fixed base environments. However, price updating introduces a new dimension that may produce confusion if not accounted for. Probably the most notorious difficulty generated by the introduction of chain-linked indices to the measurement of GDP has been that the aggregate is not the direct sum of its components, thus not only making it harder to explain its behaviour but also making it more cumbersome to work with the series in a consistent manner. Because of the non-additivity of the components, one of the processes that have been affected is that of the indirect seasonal adjustment. This document presents a consistent framework to identify and track down the sources of seasonal effects to its components in an aggregate measure chain-linked using the annual overlap method. This is done based on the decomposition of component’s contributions and the indirect seasonal adjustment. The framework allows separating the effects on growth rates into non-systematic seasonal effects, systematic seasonality and changes in systematic seasonality.

Suggested Citation

  • Cobb, Marcus, 2014. "Identifying the Sources of Seasonal Effects in an indirectly adjusted Chain-Linked Aggregate: A Framework for the Annual Overlap Method," MPRA Paper 58033, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:58033
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/58033/1/MPRA_paper_58033.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Charles Steindel, 1995. "Chain-weighting: the new approach to measuring GDP," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 1(Dec).
    3. Cobb, Marcus, 2014. "Explaining GDP Quarterly Growth from its Components in the Context of the Annual Overlap Method: A Comparison of Approaches," MPRA Paper 58022, University Library of Munich, Germany.
    4. 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.
    5. Marcus Cobb, 2014. "GDP Forecasting Bias due to Aggregation Inaccuracy in a Chain- Linking Framework," Working Papers Central Bank of Chile 721, Central Bank of Chile.
    6. Marcus Cobb & Maribel Jara, 2013. "Ajuste estacional de series macroeconómicas chilenas," Economic Statistics Series 98, Central Bank of Chile.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giancarlo Bruno & Edoardo Otranto, 2006. "The choice of time interval in seasonal adjustment: A heuristic approach," Statistical Papers, Springer, vol. 47(3), pages 393-417, June.
    2. Mauricio Gallardo & Hernán Rubio, 2009. "Diagnóstico de estacionalidad con X-12-ARIMA," Economic Statistics Series 76, Central Bank of Chile.
    3. Hall, Viv B & Thomson, Peter, 2022. "A boosted HP filter for business cycle analysis: evidence from New Zealand’s small open economy," Working Paper Series 9473, Victoria University of Wellington, School of Economics and Finance.
    4. Kroes, James R. & Manikas, Andrew S. & Gattiker, Thomas F., 2018. "Operational leanness and retail firm performance since 1980," International Journal of Production Economics, Elsevier, vol. 197(C), pages 262-274.
    5. Quenneville, Benoit & Ladiray, Dominique & Lefrancois, Bernard, 2003. "A note on Musgrave asymmetrical trend-cycle filters," International Journal of Forecasting, Elsevier, vol. 19(4), pages 727-734.
    6. Massmann, Michael & Mitchell, James, 2003. "Reconsidering the evidence: Are Eurozone business cycles converging," ZEI Working Papers B 05-2003, University of Bonn, ZEI - Center for European Integration Studies.
    7. Hai Yue Liu & Xiao Lan Chen, 2017. "The imported price, inflation and exchange rate pass-through in China," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1279814-127, January.
    8. Henryk Gurgul & Marcin Suder, 2013. "The Properties of ATMs Development Stages - an Empirical Analysis," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 14(3), pages 443-466, September.
    9. Carlos A. Medel, 2018. "A Comparison Between Direct and Indirect Seasonal Adjustment of the Chilean GDP 1986–2009 with X-12-ARIMA," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 47-87, April.
    10. Stefania D'Amico & Athanasios Orphanides, 2008. "Uncertainty and disagreement in economic forecasting," Finance and Economics Discussion Series 2008-56, Board of Governors of the Federal Reserve System (U.S.).
    11. 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.
    12. M. Angeles Carnero & Siem Jan Koopman & Marius Ooms, 2003. "Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices," Tinbergen Institute Discussion Papers 03-071/4, Tinbergen Institute.
    13. Flávio de Freitas Val & Wagner Piazza Gaglianone & Marcelo Cabus Klotzle & Antonio Carlos Figueiredo Pinto, 2017. "Estimating the Credibility of Brazilian Monetary Policy using Forward Measures and a State-Space Model," Working Papers Series 463, Central Bank of Brazil, Research Department.
    14. Viv B. Hall & Peter Thomson, 2021. "Does Hamilton’s OLS Regression Provide a “better alternative” to the Hodrick-Prescott Filter? A New Zealand Business Cycle Perspective," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(2), pages 151-183, November.
    15. Carlos A. Medel, 2018. "Forecasting Inflation with the Hybrid New Keynesian Phillips Curve: A Compact-Scale Global VAR Approach," International Economic Journal, Taylor & Francis Journals, vol. 32(3), pages 331-371, July.
    16. Møller, Niels Framroze & Møller Andersen, Frits, 2015. "An econometric analysis of electricity demand response to price changes at the intra-day horizon: The case of manufacturing industry in West Denmark," MPRA Paper 66178, University Library of Munich, Germany, revised 15 Aug 2015.
    17. 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.
    18. Ghoddusi, Hamed, 2016. "Integration of physical and futures prices in the US natural gas market," Energy Economics, Elsevier, vol. 56(C), pages 229-238.
    19. Guy Mélard, 2016. "On some remarks about SEATS signal extraction," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 53-98, March.
    20. Singh, B. Karan & Kanakaraj, A. & Sridevi, T.O., 2011. "Revisiting the empirical existence of the Phillips curve for India," Journal of Asian Economics, Elsevier, vol. 22(3), pages 247-258, June.

    More about this item

    Keywords

    Seasonal Adjustment; Annual Overlap; Chain-linking;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:58033. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.