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Time Series Modelling of Daily Tax Revenues

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  • Siem Jan Koopman
  • Marius Ooms

Abstract

We provide a detailed discussion of the time series modelling of daily tax revenues. The mainfeature of daily tax revenue series is the pattern within calendar months. Standard seasonal timeseries techniques cannot be used since the number of banking days per calendar month varies andbecause there are two levels of seasonality: between months and within months.We start the analysis with a periodic regression model with time varying parameters.This modelis then extended with a component for intra-month seasonality, which is specified as a stochasticcubic spline. State space techniques are used for recursive estimation and evaluation as they allowfor irregular spacing of the time series.The model is recently made operational and used for daily forecasting at the Dutch Ministry ofFinance. For this purpose a front-end for model configuration and data input is implemented withVisual C++, while statistical tools and graphical diagnostics are built around Ox and SsfPack. Wepresent the current model and forecasting results up to December 1999. The model and itsforecasts are evaluated.

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Bibliographic Info

Article provided by Netherlands Society for Statistics and Operations Research in its journal Statistica Neerlandica.

Volume (Year): 57 (2003)
Issue (Month): 4 ()
Pages: 439-469

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Handle: RePEc:bla:stanee:v:57:y:2003:i:4:p:439-469

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References

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  1. Harvey, Andrew & Koopman, Siem Jan & Riani, Marco, 1997. "The Modeling and Seasonal Adjustment of Weekly Observations," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 354-68, July.
  2. Ooms, M. & Franses, Ph.H.B.F., 1998. "A seasonal periodic long memory model for monthly river flows," Econometric Institute Research Papers EI 9842, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  3. Neil Shephard & Jurgen Doornik & Siem Jan Koopman, 1998. "Statistical algorithms for models in state space using SsfPack 2.2," Economics Series Working Papers 1998-W06, University of Oxford, Department of Economics.
  4. Harvey, Andrew C & Koopman, Siem Jan, 1992. "Diagnostic Checking of Unobserved-Components Time Series Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 377-89, October.
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Cited by:
  1. Clive Bowsher & Roland Meeks, 2008. "The Dynamics of Economic Functions: Modelling and Forecasting the Yield Curve," OFRC Working Papers Series 2008fe24, Oxford Financial Research Centre.
  2. Siem Jan Koopman & Marius Ooms, 2004. "Forecasting Daily Time Series using Periodic Unobserved Components Time Series Models," Tinbergen Institute Discussion Papers 04-135/4, Tinbergen Institute.
  3. 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.
  4. Eliana González & Luis F. Melo & Luis E. Rojas & Brayan Rojas, 2010. "Estimations of the natural rate of interest in Colombia," BORRADORES DE ECONOMIA 007667, BANCO DE LA REPÚBLICA.
  5. 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.

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