scholarly journals The Ways of Modelling and Forecasting Profit Tax Revenue in Lithuania

Ekonomika ◽  
2006 ◽  
Vol 73 ◽  
Author(s):  
Elena Mačiulaitytė

The aim of this paper is to propose the ways of profit tax revenue modelling and forecasting when changes of the legitimate order are considered in time series modelling. To this end, profit tax-related legislative changes are reviewed in the first part of the paper. The basic elements of the profit tax, such as the tax object, subject, the order of carryover of losses, tax rates, methods of computing a profit tax advance payment, due dates of a yearly profit tax have been changed several times over the period of profit tax application. The second part of the paper presents the stages of profit tax revenue modelling. At the first stage, the indicator of profit is suggested to be modelled and forecast using a linear regression of economic indicators. At the second stage, the function of profit tax revenue, depending on the profit indicator and other different legitimate elements of profit tax, has to be found.

Tax revenue modelling and forecasting is very crucial for revenue collection and tax administration management. The dynamics of heteroscedasticity in the financial time series (tax revenue) in the domain of technique used to model and predict tax revenue in the emerging economy threw us to this investigation. The reviews are categorized into two the tax revenue and stock exchange index. Five factors were considered in this studies modelling, forecasting, linear model, nonlinear model and heteroscedasticity, it is on this note that we syntheses over 75 studies from the literature to consider the pattern of reporting tax revenue and stock market index. Thus, from the reviewed literature, we inferred that the pattern of reporting tax revenue data and the analytical techniques employed by most of these studies are responsible for the instability (volatility) in the financial time series forecasting. Also, results revealed that linear models are mostly applied to tax revenue data with fewer non-linear models, while combination and single non-linear models were mostly used for stock exchange data. Thus, we recommend the combination of linear and nonlinear models for both tax revenue and stock exchange data which can minimize the error of heteroscedasticity in the forecasting of tax revenue in a developing economy.


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