Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2018.1442236
Forecasting tax revenues using time series techniques – a case of Pakistan
Dalia Streimikiene
; Institute of Sport Science and Innovations, Kaunas, Lithuania
Ahmed Rizwan Raheem
; Faculty of Managment Sciences, Indus University, Gulshan, Pakistan
Jolita Vveinhardt
; Institute of Sport Science and Innovations, Kaunas, Lithuania
Saghir Pervaiz Ghauri
; Faculty of Managment Sciences, Indus University, Gulshan, Pakistan
Sarwar Zahid
; Department of Business Administration, Bahria University, Islamabad, Pakistan
Sažetak
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.
Ključne riječi
Tax revenue; Time series model; A.R. model; A.R.I.M.A. model; V.A.R. model; R.M.S.E. test; Granger causality
Hrčak ID:
206072
URI
Datum izdavanja:
3.12.2018.
Posjeta: 2.077 *