Application of OLS Regression and VAR Models to Analyse the Economies of Varying Political Regimes
Bibliographic record
Abstract
The GDP per capita is a popular method of measuring the economic success of a country. This paper uses regression analysis to predict the GDP per capita (GDPPC) of countries using different independent variables. We applied Ordinary Linear Squares Regression and Vector Autoregression to check for a correlation between the chosen independent variables (Corruption Perception Index, Political Rights score, Civil Liberties score, Gender Inequality Index, Consumer Price Index, Population Density, and the percentage of people using the Internet) and the GDPPC. Using empirical evidence, we determine which model might be more accurate to attain this goal. Four countries of varying political regimes are studied - USA and Canada are categorised as democratic countries and China and Russia are non-democratic countries. Our results show trends in the correlations between the independent and dependent variables, and we can draw a distinction between the political regimes. We found that Corruption Perception Index and Population Density negatively correlates with the GDPPC of all 4 countries. We also noticed that the percentage of people using the internet and Gender Inequality Index correlates negatively with the GDPPC for non-democratic countries and in democratic countries the Consumer Price Index negatively influences the economy.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".