Time series modelling methods to forecast the volume of self-assessment tax returns in the UK

Garo Panikian, Gabby Colmenares Reverol, Jayne Rhodes, Emma McLarnon, Sarah Keast and Kokouvi Gamado

Journal of Applied Statistics, 2022, vol. 49, issue 14, 3732-3749

Abstract: Her Majesty's Revenue and Customs (HMRC) has the ambitious target of making tax digital for all its customers and collecting tax in a more efficient, effective and accurate manner for both the government and UK taxpayers. Self-assessment tax returns, the biggest key business event for HMRC, is also one of the most popular digital services with over 90% of the approximately 12 million taxpayers in self assessment filing their return online each year. The majority of returns are filed in January immediately prior to the self-assessment deadline (31st January), putting significant pressure not only on the self-assessment digital service but also on all other HMRC digital services. Hence, understanding and predicting demand for the system is vital to provide a robust and responsive service. We therefore developed mathematical models with Bayesian inference techniques to forecast volumes of Self-assessment (SA) returns submitted online during January, providing accurate hourly predictions of traffic on the digital system in the run up to the SA deadline. Because none of the models being considered is believed to be the true model, we use an ensemble modelling technique that combines forecasts from different models to develop a less risky demand forecast.

Date: 2022
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DOI: 10.1080/02664763.2021.1953448

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