Time Series Modelling of Daily Tax Revenues
نویسندگان
چکیده
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. Address for correspondence: Marius Ooms, Department of Econometrics and Operations Research, Free University Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands, E-mail: [email protected]
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