Worse than you think: Public debt forecast errors in advanced and developing economies
Bibliographic record
Abstract
Abstract We compile a unique dataset of medium‐term public debt forecasts for an unbalanced panel of 174 countries, based on International Monetary Fund (IMF) (for the period 1995–2020) and Economist Intelligence Unit (2007–2020) projections. We find that, on average, (i) there is a positive forecast error (FE) in the debt‐to‐gross domestic product (GDP) projections—that is, realized debt ratios are larger than forecasts; (ii) the FE increases with the projection horizon and is statistically significant and large—about 10% of GDP at the 5‐year horizon; (iii) the magnitude is similar between advanced economies (AEs) and emerging markets and developing economies (EMDEs) and in EMDEs is present irrespective of recessions while for AEs is associated with surprise recessions in the forecast horizon; (iv) FEs are not statistically different between IMF program and non‐program cases; and (v) positive FEs are only partly attributable to optimism about growth or the fiscal balance. Looking at the correlates of FEs, we find that FEs are larger during periods of recession, elections, fiscal stress, and high uncertainty and in countries with more economic volatility and public debt.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".