Abstract

The study was conducted to observe the effectiveness of Total Revenue (TR_t), Tax Revenue (TXR_t) and Non-Tax Revenue (NTR_t) of Fiscal Policy Dynamics towards GDP Growth Rate of Pakistan for the time period ranging 1991-92 to 2021-22, wherein Results of Augmented Dickey Fuller Test towards LGDP revealed dependent variable (LGDP_t) was stationary at level 1(0), respective independent variables (TR_t, TXR_t, NTR_t) were stationary at 1^st difference I(1). Findings of Autoregressive Distributed revealed that lag values of Total Revenue (P<0.10) impacted positive and significant influence, whereas Tax Revenue (P<0.10) and Non-Tax Revenue (P<0.10) impacted negative and significant influence on GDP Growth Rate of Pakistan. Results of Bound Test indicated that test is inconclusive. The value of Co-integrating equation was negative (-0.725495) and significant provides speed of adjustment indicating that there was convergence from short run dynamics towards long run equilibrium. Centered VIF values of tested variables (i.e TE_t, CE_t, DE_t) are less than 10 revealing non-existence of severe Multicollinearity in the Model. Heteroskedasticity test revealed the presence of homoskedasticity (no heteroskedasticity) in the model. F-Statistics value of LM Test revealed no serial correlation/ no autocorrelation in the model. F-Statistics value in case of Normality test revealed that sample data drawn from normally distributed population. Granger Causality Test revealed uni-directional causal relationship between LGDP and Total Revenue (P<0.05), between LGDP and Non-Tax Revenue (P<0.05), between Total Revenue and Tax Revenue (P<0.05) and between Non-Tax Revenue and Tax Revenue (P<0.10) indicating long-term relationship in the cointegration test, whereas no causality existed among rest of other combinations in the model. Since negative as well as positive responses existed in all three responses, so shock to LGDP noticed symmetric impact of Total Revenue, Tax Revenue and Non-Tax Revenue in Pakistan in short as well as in long run. Wald test confirmed the set of independent variables (i.e TR_t, TXR_t,, NTR_t,) were significant for a model.