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Time Series Modelling of Daily Tax Revenues

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Author Info
Siem Jan Koopman () (Vrije Universiteit Amsterdam)
Marius Ooms () (Vrije Universiteit Amsterdam)

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Abstract

We provide a detailed discussion of the time series modelling of daily tax revenues. The main feature of daily tax revenue series is the pattern within calendar months. Standard seasonal time series techniques cannot be used since the number of banking days per calendar month varies and because 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 model is then extended with a component for intra-month seasonality, which is specified as a stochastic cubic spline. State space techniques are used for recursive estimation and evaluation as they allow for irregular spacing of the time series.
The model is recently made operational and used for daily forecasting at the Dutch Ministry of Finance. For this purpose a front-end for model configuration and data input is implemented with Visual C++, while statistical tools and graphical diagnostics are built around Ox and SsfPack. We present the current model and forecasting results up to December 1999. The model and its forecasts are evaluated.

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Publisher Info
Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 01-032/4.

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Date of creation: 23 Mar 2001
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Handle: RePEc:dgr:uvatin:20010032

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Web page: http://www.tinbergen.nl/

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. 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.
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  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. Ooms, M. & Franses, Ph.H.B.F., 1998. "A seasonal periodic long memory model for monthly river flows," Econometric Institute Report EI 9842 Revision_Date: 20, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
  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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  1. 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," Working Paper Series 142, European Central Bank. [Downloadable!]
  2. 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 España Working Papers 0211, Banco de España. [Downloadable!]
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