Fiscal Marksmanship Ex-ante to Fiscal Rules in India: An Empirical Investigation
Bibliographic record
Abstract
We analyse the fiscal marksmanship of the macro-fiscal variables, ex-ante to the formulation of fiscal rules in India. The fiscal marksmanship is the accuracy of budgetary forecasting. The fiscal rules have been legally mandated in India in the form of fiscal responsibility and budget management Act (FRBM Act) in 2003, with a criteria of fiscal-deficit threshold ratio of 3 per cent of GDP and also phasing out of revenue deficit. Using Theil’s inequality coefficient (U) based on the mean square prediction error, the paper estimates the magnitude of errors in the budgetary forecasts in India during the period ex-ante to fiscal rules, and also decomposed the errors into biasedness, unequal variation and random components to analyze the source of error. The proportion of error due to random variation has been significantly higher (which is beyond the control of the forecaster), while the errors due to bias has been negligible in the period prior to fiscal rules in India. The analysis related to efficiency of forecasts also showed that no significant improvement in forecasts over time prior to fiscal responsibility and budget management (FRBM) Act.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".