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

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