Time Series Modelling of Daily Tax Revenues
AbstractWe 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 InfoArticle provided by Netherlands Society for Statistics and Operations Research in its journal Statistica Neerlandica.
Volume (Year): 57 (2003)
Issue (Month): 4 ()
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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0039-0402
Other versions of this item:
- 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.
- Siem Jan Koopman & Marius Ooms, 2001. "Time Series Modelling of Daily Tax Revenues," Tinbergen Institute Discussion Papers 01-032/4, Tinbergen Institute.
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.:
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