Bibliographic record
Abstract
This study analyzes international experience in measuring fiscal policy uncertainty and provides quantitative estimates of government spending uncertainty and tax policy uncertainty for Russia from April 2011 to June 2024. The analysis of empirical studies showed that the main periods of fiscal policy uncertainty in the international context are periods of political instability and elections, fiscal and budget debates, large budget deficits, economic crises, and external shocks such as the Gulf wars and the coronavirus pandemic in early 2020. Google Trends searches on “fiscal policy” and time-varying volatility of government expenditures calculated on the basis of stochastic volatility models are used to estimate fiscal policy uncertainty on Russian data. Composite indices of fiscal, budget and tax policies uncertainty based on internet queries on Google were obtained using principal component analysis. Stochastic volatility models were estimated for the fiscal instrument on a quarterly basis. The results of the empirical analysis made identified key periods of fiscal policy uncertainty for Russia during the second quarter of 2011 to the second quarter of 2024: the coronavirus pandemic in 2020 and the geopolitical risks in 2022.
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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 | 0.008 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".