Investigating the Effect of Government Revenue uncertainty on the Budget Deficit (Quantile Regression Approach Based on Wavelet Transform)

Articles in Press

Document Type : Original Article

Authors

1 Associate Professor, Department of Economics, Payame Noor University,Tehran, Iran

2 Assistant Professor, Department of Economics, Payame Noor University,Tehran, Iran

3 PhD student in Economics, Islamic Azad University, Science and Research Branch, Tehran, Iran

10.22075/jem.2025.36087.1953

Abstract

In the real world, the uncertainty of economic variables causes risk in the decision-making space of governments and economic factors for applying policies and annual budgeting and affects their behavior, so conceptualizing the tax revenue model as a part of government revenue will be useful. The present study examines the quantitative effects of the uncertainty of government revenues on the budget deficit in Iran's economy using the generalized autoregression model of conditional variance in the period from 2016 to 2016. For the first time, for a more accurate analysis of fluctuations, the discontinuous wavelet transform method with maximum overlap was used to determine the effects of income uncertainty on the budget in different time scales. For Iran, the results of the analysis show that the uncertainty of government revenues has a positive and significant effect on the budget deficit in all parameters. This effect is lower in the initial quantiles and higher in the final quantiles, which indicates the increasing positive effect of government revenue uncertainty on the budget deficit over time. These results emphasize that managing uncertainty in government revenues, especially in the short and medium term, is critical. Economic policies that can help reduce dependence on oil revenues and increase the diversity of government revenue sources can play a key role in reducing the budget deficit and improving economic stability.

Keywords

Journal of Econometric Modelling

Articles in Press, Accepted Manuscript
Available Online from 21 April 2025

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