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Time-Series Modelling of Daily Tax Revenues

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  • Marius Ooms

    ()
    (Erasmus University, Rotterdam)

  • Bj�rn de Groot

    ()
    (Erasmus University, Rotterdam)

  • Siem Jan Koopman

    ()
    (CentER, Tilburg)

Abstract

This paper discusses a time-series model for daily tax revenues. The model is an unobserved-components model with trend and seasonal components that vary over time. The seasonalities for inter-month and intra-month movements are modelled using stochastic cubic splines. The model is made operational and used to produce daily forecasts at the Dutch Ministry of Finance. A front-end for model configuration and data input is implemented with Visual C ++, while the econometrics and graphical diagnostics are build around Ox and SsfPack, the latter of which implements general procedures for the Kalman filter and state-space models.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 312.

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Date of creation: 01 Mar 1999
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Handle: RePEc:sce:scecf9:312

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  1. 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.
  2. 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.
  3. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
  4. 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.
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Cited by:
  1. Clive G. Bowsher & Roland Meeks, 2008. "The Dynamics of Economic Functions: Modelling and Forecasting the Yield Curve," Economics Papers 2008-W05, Economics Group, Nuffield College, University of Oxford.
  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. Eliana González & Luis F. Melo & Luis E. Rojas & Brayan Rojas, . "Estimations of the natural rate of interest in Colombia," Borradores de Economia 626, Banco de la Republica de Colombia.
  4. 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.
  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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