A Simulation Model of Federal, Provincial and Territorial Government Accounts for the Analysis of Fiscal-Consolidation Strategies in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it