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Notes on Time Series Analysis, ARIMA Models and Signal Extraction

Author

Listed:
  • Regina Kaiser

    () (Universidad Carlos III de Madrid)

  • Agustín Maravall

    () (Banco de España)

Abstract

Present practice in applied time series work, mostly at economic policy or data producing agencies, relies heavily on using moving average filters to estimate unobserved components in time series, such as the seasonally adjusted series, the trend, or the cycle. The purpose of the present paper is to provide an informal introduction to the time series analysis tools and concepts required by the user or analyst to understand the basic methodology behind the application of filters.

Suggested Citation

  • Regina Kaiser & Agustín Maravall, 2000. "Notes on Time Series Analysis, ARIMA Models and Signal Extraction," Working Papers 0012, Banco de España;Working Papers Homepage.
  • Handle: RePEc:bde:wpaper:0012
    as

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    File URL: http://www.bde.es/f/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosTrabajo/00/Fic/dt0012e.pdf
    File Function: First version, 2000
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    References listed on IDEAS

    as
    1. Prescott, Edward C., 1986. "Theory ahead of business-cycle measurement," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 25(1), pages 11-44, January.
    2. David A. Pierce, 1978. "Seasonal adjustment when both deterministic and stochastic seasonality are present," Special Studies Papers 107, Board of Governors of the Federal Reserve System (U.S.).
    3. David A. Pierce, 1978. "Seasonal Adjustment When Both Deterministic and Stochastic Seasonality Are Present," NBER Chapters,in: Seasonal Analysis of Economic Time Series, pages 242-280 National Bureau of Economic Research, Inc.
    4. Burridge, Peter & Wallis, Kenneth F, 1984. "Unobserved-Components Models for Seasonal Adjustment Filters," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 350-359, October.
    5. 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.
    6. Maravall, Agustin & Planas, Christophe, 1999. "Estimation error and the specification of unobserved component models," Journal of Econometrics, Elsevier, vol. 92(2), pages 325-353, October.
    7. Gomez, Victor, 1999. "Three Equivalent Methods for Filtering Finite Nonstationary Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 109-116, January.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Alfredo Baldini, 2005. "Fiscal Policy and Business Cycles in an Oil-Producing Economy; The Case of Venezuela," IMF Working Papers 05/237, International Monetary Fund.
    2. Aslihan Atabek & Oguz Atuk & Evren Erdogan Cosar & Cagri Sarikaya, 2009. "Mevsimsel Modellerde Calisma Gunu Degiskeni," CBT Research Notes in Economics 0903, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    3. Buss, Ginters, 2010. "Seasonal decomposition with a modified Hodrick-Prescott filter," MPRA Paper 24133, University Library of Munich, Germany.
    4. Oguz Atuk & Beyza Pinar Ural, 2002. "Seasonal Adjustment Methods : An Application to the Turkish Monetary Aggregates," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 2(1), pages 21-37.
    5. Gianluca Caporello & Agustín Maravall & Fernando J. Sánchez, 2001. "Program TSW Reference Manual," Working Papers 0112, Banco de España;Working Papers Homepage.

    More about this item

    Keywords

    time series; economic policy; seasonal fluctuations;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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