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Notes on Time Series Analysis, ARIMA Models and Signal Extraction

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Author Info

  • 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.

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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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Bibliographic Info

Paper provided by Banco de Espa�a in its series Banco de Espa�a Working Papers with number 0012.

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Length: 73 pages
Date of creation: 2000
Date of revision:
Handle: RePEc:bde:wpaper:0012

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Related research

Keywords: time series; economic policy; seasonal fluctuations;

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References

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  1. Gomez, Victor, 1999. "Three Equivalent Methods for Filtering Finite Nonstationary Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 109-16, January.
  2. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, Fall.
  3. Edward C. Prescott, 1986. "Theory ahead of business cycle measurement," Staff Report 102, Federal Reserve Bank of Minneapolis.
  4. 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.
  5. Burridge, Peter & Wallis, Kenneth F, 1983. "Unobserved-Components Models for Seasonal Adjustment Filters," The Warwick Economics Research Paper Series (TWERPS) 244, University of Warwick, Department of Economics.
  6. 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.
  7. 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.).
  8. 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 127-52, April.
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Citations

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Cited by:
  1. Alfredo Baldini, 2005. "Fiscal Policy and Business Cycles in an Oil-Producing Economy," IMF Working Papers 05/237, International Monetary Fund.
  2. 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.
  3. 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.
  4. Buss, Ginters, 2010. "Seasonal decomposition with a modified Hodrick-Prescott filter," MPRA Paper 24133, University Library of Munich, Germany.
  5. Gianluca Caporello & Agustín Maravall & Fernando J. Sánchez, 2001. "Program TSW Reference Manual," Banco de Espa�a Working Papers 0112, Banco de Espa�a.

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