How to Forecast Corporate Income Tax Revenues

Sebastian Beer, Brian Erard and Tibor Hanappi

No 2025/010, IMF Fiscal Affairs Department from International Monetary Fund

Abstract: Corporate income tax (CIT) collections are among the most difficult revenues to forecast—even with adequate staffing, comprehensive data, and a stable tax design. In practice, forecasting units typically operate under less ideal conditions. As institutional constraints take time to ease, this Note sets out a practical toolkit of methods to strengthen forecasting capacity across a wide range of country contexts. It outlines techniques that provide unbiased forecasts even when the impact of past reforms is only partially known, introduces approaches to account for ongoing and prospective policy changes to leverage time-series approaches, and highlights the potential efficiency gains achievable through structural modeling. A simple empirical assessment of forecasting specifications shows that parsimonious regression models, when backed by sufficient data, can improve prediction accuracy, even though the benchmark of assuming CIT revenues grow in line with GDP remains difficult to beat.

Keywords: Corporate Income Tax; Tax Revenue Forecasting; Fiscal Policy; Tax Policy Changes; Economic Forecasting; Tax Base Elasticity; Microsimulation Models; Cit revenue; time-series approach; prediction accuracy; Corporate income tax receipt; time-series method; Income tax systems; Corporate taxes; Income and capital gains taxes; Global (search for similar items in EconPapers)
Pages: 42
Date: 2025-11-24
New Economics Papers: this item is included in nep-for, nep-pbe and nep-pub
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