A Simulation Model of Federal, Provincial and Territorial Government Accounts for the Analysis of Fiscal-Consolidation Strategies in Canada
Bibliographic record
Abstract
This paper presents a simulation model of the main budget aggregates of federal, provincial and territorial governments in Canada. The general approach is to use a cyclical indicator (output gap), estimate the sensitivity of government revenue and expenditure to this cyclical indicator using historical data, and use projections of the cyclical indicator to simulate budgetary outcomes under various economic scenarios. Provincial/territorial annual output gaps are estimated going back to 1984. These are used to jointly estimate for all governments the historical sensitivities of the main revenue and expenditure categories to provincial/territorial economic cycles using Seemingly Unrelated Regressions. Projections of potential output by province and territory are then made to 2020 and a multitude of paths for the evolution of provincial/territorial output gaps are generated to 2020. These output gap paths serve as bases for simulating medium-term fiscal outcomes under a variety of possible economic scenarios, allowing the construction of probability densities for fiscal outcomes. The paper also contains an analysis of the cyclicality of Canadian governments’ fiscal policies between 1984 and 2007. Several jurisdictions are found to have had pro-cyclical fiscal policies over this period.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".