Forecasting Patterns of Income Tax:Comparative Analysis

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Forecasting Patterns of Income Tax:Comparative Analysis

Journal Title: Ekonomika ir Vadyba: Aktualijos ir Perspektyvos - Year 2013, Vol 32, Issue 4

Abstract

The analysis of the income tax impact on the government revenue revealed two scientific approaches: under one consideration - this tax must be withdrawn because it slows down the growth of economics and increases the shadow economy; others argue that the tax is beneficial for the state budget and brings no negative consequences. In the near future, the income tax withdrawal idea will not be implemented in Lithuania, considering an important role it plays on the state yield. This type of tax is one of the four major corporate taxes in Lithuania. During the period, the state income from this tax varied from 6 to 12 per cent. LITMOD tax forecast model is applied in Lithuania; however, the income tax forecast done by it is inaccurate, deviations during the period varied from 2 to 28 per cent. As a scientific issue, the forecast on income tax receipts has been scarcely analysed by researchers. Most research has been carried out by Budryte, Maciulaityte and Ruskyte, Rutkauskas, Navickas. Researchers differentiate the following patterns: macroeconomic, elasticity, micro simulating, extrapolated, and integrated. Macroeconomic models are most often applied in the state taxation forecast, income included. By applying these patterns, it was established that basic macroeconomic data, namely: labour, capital, products and services, finance, and foreign trade, have impact on corporate profits. In order to improve the forecasting patterns on income from the income tax in separate countries, more comprehensive research regarding the choice of indicators is necessary. Factoring analysis or other suitable methods for specific data selection that have impact on income tax receipts are recommended. Elasticity patterns are most often used for the assessment of taxation policy changes and their impact. Here changes of tax revenue or tariffs are included, additionally, the impact on other economic data is analysed. To improve the evaluation of factor impact on the outcome and to verify the suitability of selected factors for the revenue on income tax forecast, the Granger method is used. Introduction of micro simulating patterns is appropriate when the impact of regulation changes on the structure of the tax base, identification of individual entities and the amounts payable by them, and provision of general budget tax revenues, as well as modeling of the fiscal policy in the country, are assessed. Extrapolated forecast methods can be used in tax revenue modeling, however in this case, extra economic indicators are not included. By applying two forecast patterns for forecasting revenue on the income tax in Lithuania, multiple regression and time series, as the most common application for this type of research, was used; it was found that the time series model can be applied in certain economic cycles because it cannot predict a sharp increase in income or loss. Application of the multiple regression pattern improving/worsening economic factors would signal about the changing economic situation, while at the same time, would warn about the revenue collection direction. This may be proven by making forecast on revenue from the income tax with a limited growth trend and multiple regression models. Keywords: income tax, budget revenu, model, forecasting.

Authors and Affiliations

Danutė Binkienė , Astrida Slavickienė

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EP ID EP162501
DOI -
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