Iraqi Public Debt Dynamics and Its Fiscal Sustainability in the Context of SVAR and Shock Assessment

This study examines the dynamic interactions among gross domestic product (GDP), external public debt, and domestic public debt in Iraq and discusses their implications for fiscal sustainability. The observed annual data cover the period 2005–2024, while the values for 2025 are based on IMF projections. Because continuous official monthly observations were unavailable, the annual series were temporally disaggregated using cubic-spline interpolation to construct an analytical monthly series. These interpolated values do not represent additional independent observations; therefore, the empirical findings are interpreted as exploratory. A recursive Structural Vector Autoregression (SVAR) model was estimated using Cholesky identification with the ordering GDP, external debt, and domestic debt. The variables were transformed into second differences in their natural logarithms, and a three-lag specification was employed. Impulse-response functions and forecast-error variance decomposition were used to examine the transmission and relative importance of the identified innovations. At the 24-month forecast horizon, GDP shocks explained 93.3% of GDP variation, while external debt was predominantly explained by its own shocks (86.3%). Domestic-debt variation was explained by its own shocks (47.3%), GDP shocks (44.1%), and external-debt shocks (8.6%). These results suggest that domestic debt is more closely associated with changes in domestic economic activity, whereas external debt follows a comparatively persistent path. The findings emphasize the importance of debt composition, non-oil revenue diversification, expenditure management, and coordination between domestic and external borrowing. Because oil revenues and government expenditure are not included as separate endogenous variables, the model does not directly identify oil-revenue or government-spending shocks. Future research should employ genuinely observed quarterly or monthly data and incorporate these fiscal variables explicitly.