Forecasting tax revenues under the simplified taxation system based on a linear regression model: Analysis of key factorsStrategic priorities for the development of Russia's electric grid complex
Subject. Tax revenues from the simplified taxation system (STS) as a key component of the revenue side of the Novosibirsk Oblast’s budget and an object for assessing tax expenditures in accordance with federal and regional legislation. Objectives. To identify and quantify factors that have a statistically significant impact on the dynamics of STS revenues; to develop a forecast model compliant with the requirements of legal regulations on tax expenditure assessment; and to ensure a scientifically grounded approach to forecasting budget revenues and evaluating the effectiveness of tax incentives. Methods. The study employs the multiple linear regression method, including analysis of correlation links, testing for autocorrelation, and assessing the statistical significance of coefficients (using Student’s t-test) and the overall significance of the model (using Fisher’s F-test). Results. It has been revealed that STS revenues in the region are primarily determined by macroeconomic activity: retail trade turnover and the volume of paid services to the population act as the key statistically significant predictors. The constructed multiple linear regression model is characterized by a high coefficient of determination, which confirms its suitability for forecasting budget revenues. Conclusions and Relevance. The resulting model enables a shift from qualitative assessments to quantitative forecasting, thereby enhancing the transparency and validity of the budgeting process. The findings can be used to improve methods for assessing tax expenditures, develop budget forecasts, and substantiate tax policy at the regional level.
Keywords: tax expenses, budget forecasting, multiple linear regression
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