AN EFFICIENT MATHEMATICAL MODEL TO ESTIMATE THE FISCAL DEFICIT

For decades, fiscal deficits have been the main issue for most of the developing countries, and fiscal gaps have widened due to low revenue collection and rapid expansion of expenditures. This paper discusses the development of a mathematical model based on Multiple Regression Analysis to predict the Income Tax Revenue of Pakistan and examine the factors that affect it. Empirical data of Crude Oil Price (COP), Inflation Rate (CPI), Real Gross Domestic Product Growth Rate (GDPGR), Unemployment rate (UEMR), and Employed Population (EMP) from 1981 to 2014 have been utilized as the main factor of ITR. To verify the basic assumptions residual analysis is performed in detail. The model's normality, linearity, homoscedasticity and goodness of fit are also assessed. While the coefficient of determination (R2=0.975) indicates the performance of the model and validated by comparing the actual values of ITR with those predicted from the model. The results indicate a good agreement between the real and expected values, suggesting the model is suitable for estimating ITR. However, the employed population is the most influential model variable in predicting the ITR. Moreover, the model is considered important for evaluating future changes in ITR of Pakistan and support the government to play a noteworthy part in the social and economic progress of a country and its inhabitants. Furthermore, this analysis may provide the government with the opportunity to investigate Pakistan's tax revenue.