Determinants of Public Debt for middle income and high income group countries using Panel Data regression
Bibliographic record
Abstract
To be able to predict when a nation will go bust has been one of toughest challenges in macroeconomics. Considerable research and effort has been put into this direction but still we are not in a position to say anything with certainty. This paper analyzes panel pool data on 31 countries across the world for the past 30 years on the basis of which the possibility of a sovereign default can be explored. The aim of this study is to understand which all factors influence the public debt in middle and high income group countries using Panel regression. Total effects model, Cross section fixed effects model, Cross section random effects model have been used to understand the factors whereas Autoregressive multiple regression model has been used to forecast the debt figures. The research findings suggest that the most important determinant of debt situation is GDP growth rate for both high and middle income group countries. In addition to this, Central government expenditure, education expenditure and Current account balance are also seen to influence the debt situation for both groups. FDI and Inflation have no impact on debt to GDP ratios among high income group countries but are found to be of more relevance when determining debt situation of middle income group countries. Population density and population above 65 years of age do not have any impact whatsoever on debt to GDP ratios of high and middle income group countries. Forecasts for weighted average public debt for high income group countries indicate steady increase. Debt situation of countries including Switzerland, Korea, Slovak rep, France and Japan is likely to worsen over the next 5 years. The debt situation of Greece and Spain is unlikely to change much whereas Ireland, USA, Canada, Italy, Hungary are expected to get better till 2015.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".