Forecasting tax revenues using time series techniques – a case of Pakistan

Abstract

The objective of this research was to forecast the tax revenue of Pakistan for the fiscal year 2016–17 using three different time series techniques and also to analyse the impact of indirect taxes on the working class. The study further analysed the efficiency of three different time series models such as the Autoregressive model (A.R. with seasonal dummies), Autoregressive Integrated Moving Average model (A.R.I.M.A.), and the Vector Autoregression (V.A.R.) model. In any economy, tax analysis and forecasting of revenues is of paramount importance to ensure the economic and fiscal policies. This study is important to identify significant variables affecting tax revenue specifically in Pakistan. The data used for this paper was from July 1985 to December 2016 (monthly) and focused on forecasting for 2017. For the forecasting of total tax revenue, we used components of tax revenues such as direct tax, sales tax, federal excise duty and customs duties. The results of this study revealed that among these models the A.R.I.M.A. model gives better-forecasted values for the total tax revenues of Pakistan. The results further demonstrated that major tax revenue is generated by indirect taxes, which cause more inflation that directly hits the working class of Pakistan.