PUBLIC DEBT SUSTAINABILITY IN INDIA: A CO-INTEGRATION APPROACH BASED ON STRUCTURAL BREAKS WITH REGIME SHIFT
Bibliographic record
Abstract
In this paper we examine the sustainability of public debt in case of India. We have used combined government data (Centre’s and State’s government) for a period from 1990 to 2016). For testing the sustainability of debt, we have taken government revenues and expenditure. First we have investigated co-integration between government spending and revenues using ARDL bound testing model. The bound test reveals that there exists no long-run relationship between the variables. Gregory-Hansen and Hatemi-j threshold co-integration test are applied to test the sustainability hypothesis in the presence of regime shift. the result shows that no co-integration relationship between the variables in case of single structural break, but for two structural break our study confirm existence of co-integration relationship for the given variables. We do not find the long-run coefficients statistically significant for sustainability of public debt.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".