如何预测企业所得税收入

Sebastian Beer,Brian Erard, andTibor Hanappi How to Forecast Corporate Income Tax Revenues NOTE/2025/010 Sebastian Beer, Brian Erard, and Tibor Hanappi* Cataloging-in-Publication Data IMF Library Names: Beer, Sebastian, author. | Erard, Brian, author. | Hanappi, Tibor, author. | International MonetaryFund, publisher.Title: How to forecast corporate income tax revenues: / Sebastian Beer, Brian Erard, and Tibor Hanappi Other titles: How to notes.Description: Washington, DC : International Monetary Fund, 2025. | Nov. 2025. | NOTE/2025/010. |Includes bibliographical references. Identifiers: ISBN: 9798229031257(paper)9798229031271(ePub)9798229031318(WebPDF) Subjects: LCSH: Tax revenue estimating. | Corporations--taxation.Classification: LCC HJ2351.B4 2025 DISCLAIMER: How to Notes offer practical advice from IMF staff members to policymakers on importantissues. The views expressed in How to Notes are those of the author(s) and do not necessarily representthe views of the IMF, its Executive Board, or IMF management. RECOMMENDED CITATION: Beer, Sebastian, Brian Erard, and Tibor Hanappi. 2025. “How to ForecastCorporate Income Tax Revenues.” IMF How to Note 2025/010, International Monetary Fund, Washington,DC. Publication orders may be placed online, by fax, or through the mail:International Monetary Fund, Publications ServicesP.O. Box 92780, Washington, DC 20090, USA Contents Introduction ............................................................................................................................................... 1Forecasting Architecture ........................................................................................................................... 4Accounting for Policy Change................................................................................................................. 11Robust Forecasting Techniques ............................................................................................................. 16Time-Series Methods.............................................................................................................................. 21 Boxes Box 1. Components of Corporate Income Tax Receipts ............................................................................... 6Box 2. Efficiency Gains from Structural Modeling....................................................................................... 10Box 3. Omitted Variable Bias from Neglecting Policy Change.................................................................... 12Box 4. Proportional Adjustment Method...................................................................................................... 14Box 5. Causes for Deviations from Unit Elasticity....................................................................................... 20 FiguresFigure 1. CIT Revenues: Contribution to Total and Relative Volatility .......................................................... 3 Figure 2. Reduced Form versus Structural Approaches ............................................................................... 8Figure 3. Forecast Performance Conditional on Income Group ................................................................. 30 TablesTable 1. Hypothetical In-Year Revenue Collections .................................................................................... 17 Table 2. Tested Models ............................................................................................................................... 28Table 3. Aggregate Forecast Performance ................................................................................................. 29Table 4. MAPE and BICS Conditional on Forecast Horizon and Estimation Sample Size ......................... 32 How to Forecast Corporate Income Tax Sebastian Beer, Brian Erard, and Tibor HanappiNovember 2025 Corporate income tax (CIT) collections are among the most difficult revenues to forecast—even withadequate staffing, comprehensive data, and a stable tax design. In practice, forecasting unitstypically operate under less ideal conditions. As institutional constraints take time to ease, this Notesets out a practical toolkit of methods to strengthen forecasting capacity across a wide range ofcountry contexts. It outlines techniques that provide unbiased forecasts even when the impact of past Introduction Accurate revenue forecasting is the basis for effective fiscal planning. Baseline estimates informstakeholders about the available budget envelope, and the potential need for reforms to implement thegovernment’s spending priorities. Forecasting errors can result in funding gaps that may require additionalpublic debt issuance or supplementary budgets, or may lead to the revocation of approved projects, CIT revenues are characterized by extreme year-over-year volatility.During the pandemic, CIT-to-GDPratios fell by an average of 24 percentage points year over year (Figure 1, panel 1). Although CI