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The Modeling and Seasonal Adjustment of Weekly Observations

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  • Harvey, Andrew
  • Koopman, Siem Jan
  • Riani, Marco

Abstract

Several important economic time series are recorded on a particular day every week. Seasonal adjustment of such series is difficult because the number of weeks varies between 52 and 53 and the position of the recording day changes from year to year. In addition certain festivals, most notably Easter, take place at different times according to the year. This article presents a solution to problems of this kind by setting up a structural time series model that allows the seasonal pattern to evolve over time and enables trend extraction and seasonal adjustment to be carried out by means of state-space filtering and smoothing algorithms. The method is illustrated with a Bank of England series on the money supply.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 15 (1997)
Issue (Month): 3 (July)
Pages: 354-68

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Handle: RePEc:bes:jnlbes:v:15:y:1997:i:3:p:354-68

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Cited by:
  1. J. Sebastián Becerra & Luis Ceballos & Felipe Córdova & Michael Pedersen, 2009. "Pass-through of Large Changes in Monetary Policy Rate – Evidence for Chile," Working Papers Central Bank of Chile 522, Central Bank of Chile.
  2. Cabrero, Alberto & Camba-Méndez, Gonzalo & Hirsch, Astrid & Nieto, Fernando, 2002. "Modelling the daily banknotes in circulation in the context of the liquidity management of the European Central Bank," Working Paper Series 0142, European Central Bank.
  3. Marius Ooms & Bj�rn de Groot & Siem Jan Koopman, 1999. "Time-Series Modelling of Daily Tax Revenues," Computing in Economics and Finance 1999 312, Society for Computational Economics.
  4. Jalles, Joao Tovar, 2009. "Structural Time Series Models and the Kalman Filter: a concise review," FEUNL Working Paper Series wp541, Universidade Nova de Lisboa, Faculdade de Economia.
  5. Marek Hlavacek & Michael Konak & Josef Cada, 2005. "The Application of Structured Feedforward Neural Networks to the Modelling of Daily Series of Currency in Circulation," Working Papers 2005/11, Czech National Bank, Research Department.
  6. Siem Jan Koopman & Marius Ooms, 2001. "Time Series Modelling of Daily Tax Revenues," Tinbergen Institute Discussion Papers 01-032/4, Tinbergen Institute.
  7. Diego Elías & Matías Vicens, 2012. "Bills and Coins Daily Demand Forecast," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(65-66), pages 23-39, September.
  8. Rodriguez, Gloria Martin & Hernandez, Jose Juan Caceres, 2005. "Evolving Seasonal Pattern of Tenerife Tomato Exports," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24501, European Association of Agricultural Economists.
  9. Rodriguez, Gloria Martin & Hernandez, Jose Juan Caceres, 2002. "Canary Island Tomato Exports: A Structural Analysis of Seasonality," 2002 International Congress, August 28-31, 2002, Zaragoza, Spain 24901, European Association of Agricultural Economists.
  10. Koopman, Siem Jan & Franses, Philip Hans, 2002. " Constructing Seasonally Adjusted Data with Time-Varying Confidence Intervals," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(5), pages 509-26, December.
  11. Höhle, Michael & Paul, Michaela, 2008. "Count data regression charts for the monitoring of surveillance time series," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4357-4368, May.
  12. Bhattacharya, Rudrani & Patnaik, Ila & Shah, Ajay, 2008. "Early warnings of inflation in India," Working Papers 08/54, National Institute of Public Finance and Policy.
  13. Alberto Cabrero & Gonzalo Camba-Mendez & Astrid Hirsch & Fernando Nieto, 2002. "Modelling the daily banknotes in circulation in the context of the liquidity management of the European Central Bank," Banco de Espa�a Working Papers 0211, Banco de Espa�a.
  14. Ito, Ryoko, 2013. "Modeling dynamic diurnal patterns in high frequency financial data," Cambridge Working Papers in Economics 1315, Faculty of Economics, University of Cambridge.

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