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Time Series Modelling of Daily Tax Revenues

  • Siem Jan Koopman
  • Marius Ooms

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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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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  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. 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.
  3. 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.
  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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