Estimating the Degree of Fiscal Dominance in a DSGE Model with Sticky Prices and Non-Zero Trend Inflation ∗
Bibliographic record
Abstract
This paper studies the interdependence between fiscal and monetary policy in a DSGE model with sticky prices and non-zero trend inflation. We characterize the fiscal and monetary policies by a rule whereby a given fraction κ of the government debt must be backed by the discounted value of current and future primary surpluses. The remaining fraction of debt is backed by seigniorage revenues. When κ = 1, there is no fiscal dominance, since the fiscal authority backs all debt and accommodates the (independent) monetary policy, by adjusting current or future primary surpluses to satisfy the government’s intertemporal budget constraint. If κ = 0, all debt is backed by the monetary authority and there is complete fiscal dominance. A continuum of possibilities lies between these two polar cases. We numerically show that: 1) the degree of fiscal dominance, as measured by (1 − κ) , is positively related to trend inflation, and 2) when prices are sticky, κ has significant effects on the business cycle dynamics. The model is estimated using Bayesian techniques. Estimates of κ imply a high degree of fiscal dominance in both Mexico and South Korea, but almost no fiscal dominance in Canada and the U.S. The country-specific estimates of the structural parameters are used in a second-order approximation of the equilibrium around the deterministic steady-state to evaluate the welfare costs of fiscal dominance. Results suggest significant welfare losses for countries with high degrees of fiscal dominance. JEL Classification: E31, E42, E50, E63
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".