Forecasting UK Income Tax

Zara Ghodsi () and Allan Webster ()
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Zara Ghodsi: Statistical Research Centre, Bournemouth University
Allan Webster: Statistical Research Centre, Bournemouth University

No BAFES07, BAFES Working Papers from Department of Accounting, Finance & Economic, 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.

Keywords: United Kingdom; Income Tax; Forecasting; Singular Spectrum Analysis; ARIMA; Exponential Smoothing; Neural Networks (search for similar items in EconPapers)
Pages: 14 pages
Date: 2017-01
New Economics Papers: this item is included in nep-cmp, nep-for and nep-pbe
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