What question did this study set out to answer?

This research aims to create a hybrid model for accurately forecasting Iraq's oil revenues and government expenditures over two decades.

March 8, 2026Open Access

Robust Wavelet-Quantile Regression for Forecasting Iraq's Oil Revenues and Government Expenditure (2004–2024): A Proposed Hybrid Statistical Model

Key Points

This research aims to create a hybrid model for accurately forecasting Iraq's oil revenues and government expenditures over two decades.
Developed a Wavelet-Quantile Regression model to capture multi-scale volatility in fiscal data.
Used Discrete Wavelet Transform for signal denoising and established quantile regression across five levels.
Assessed stationarity and diagnostics using ADF tests and other statistical procedures.
Achieved MSE of 93.17, significantly better than OLS and standalone QR techniques.
Demonstrated strong predictive capability with R² value of 0.942 and MAPE of 2.76%.
Identified a structural break in 2014, indicating shifts in fiscal behavior.

Abstract

Purpose - This study aims to develop and validate a hybrid Wavelet-Quantile Regression (W-QR) model for forecasting Iraq’s oil revenues and government expenditure over the period 2004–2024, addressing the limitations of conventional linear approaches in capturing non-stationarity, distributional asymmetry, and multi-scale volatility in petroleum-linked fiscal series. Method - The model integrates Discrete Wavelet Transform (Db4, level 3, Universal Soft thresholding) for multi-resolution signal denoising with Quantile Regression estimated at five quantile levels (τ = 0.10, 0.25, 0.50, 0.75, 0.90). Stationarity is assessed via ADF tests, and diagnostics include Breusch-Pagan, Jarque-Bera, and CUSUM procedures. Result - The W-QR model achieves MSE = 93.17, representing a 70.2% improvement over OLS and 52.9% over standalone QR, with R² = 0.942 and MAPE = 2.76%. A significant structural break is identified in 2014, and quantile slope coefficients confirm pro-cyclical fiscal behavior. Implication - The findings provide policymakers with quantile-specific fiscal projections for stress-testing under varying oil revenue scenarios, supporting fiscal consolidation and revenue diversification strategies in oil-dependent economies. Originality - This study is the first to combine wavelet denoising with quantile regression specifically calibrated for petroleum-fiscal time series in Iraq, offering a synergistic hybrid framework that surpasses both individual methods and standard econometric models. Keywords: Wavelet-Quantile Regression, Oil Revenue Forecasting, Fiscal Policy, Iraq Economy, Structural Break

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