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Forecasting UK Income Tax

Author

Listed:
  • Zara Ghodsi

    (Statistical Research Centre, Bournemouth University)

  • Allan Webster

    (Statistical Research Centre, Bournemouth University)

Abstract

The literature on forecasting tax revenues focuses on the need for a body of competing forecasts independent of government, to limit potential political bias. The Office for Budget Responsibility does provide detailed independent forecasts for the UK but there are limited alternatives. The literature on appropriate techniques for forecasting detailed tax revenues is under-developed. In many countries tax revenue forecasts are embedded in a more extensive macro-economic forecasting model. These models lack sufficient precision for revenue forecasting revenues for several specific taxes. Such models are too involved to support a body of competing independent forecasts. In consequence there is an established need for single equation revenue forecasts for specific taxes to complement the macro-economic approach. This study considers the use of a number of (mainly) time series forecasting techniques. We find Recurrent Singular Spectrum Analysis (RSSA) to perform the best of the techniques considered.

Suggested Citation

  • Zara Ghodsi & Allan Webster, 2017. "Forecasting UK Income Tax," BAFES Working Papers BAFES07, Department of Accounting, Finance & Economic, Bournemouth University.
  • Handle: RePEc:bam:wpaper:bafes07
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    File URL: https://repec.bmth.ac.uk/bam/wp/BAFES07.pdf
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    Keywords

    United Kingdom; Income Tax; Forecasting; Singular Spectrum Analysis; ARIMA; Exponential Smoothing; Neural Networks;
    All these keywords.

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