Volatility Modeling of Revenue Taxes in Nigeria Utilizing VAR with GARCH Family Models
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Accurate future values forecast is the Policymakers' tool for tax revenue planning. It’s owed to the fact that tax revenue volatility is a valuable measure of uncertainty about the economic environment of a country. This paper applies multivariate time series analysis to the monthly (N Naira) tax revenue data spanning from January 1981 to December 2018 to examine the behavior of tax revenue in Nigeria. To capture the volatility effect in tax revenue data, the paper applies vector autoregression (VAR) and GARCH family models. The study divulges that the tax revenue series displays the empirical consistencies such as non-normality non-stationarity, clustering volatility, and serial correlation that validate the application of the GARCH family methods. The study indicates that tax revenue behavior is usually induced by past data about tax revenue. It’s also inferring that previous monthly’s volatility in tax revenue can distress the present volatility of tax revenue. In addition, the estimate for asymmetric volatility suggests that positive shocks imply a higher next period conditional variance than negative shocks of the same sign. The main policy implication of these results is that since tax revenue volatility may increase administrative costs and reduce the gains to income revenue, knowledge of tax revenue volatility estimation and forecasting is significant for fiscal policy and tax revenue administrative management.
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