The N.A.F.T.A. Evidence on Testing the Kumara Swamy Theorem of Inflationary Gap
Bibliographic record
Abstract
This paper empirically tests the Kumara Swamy Theorem of the Inflationary Gap for the U.S.A, Canada and Mexico, the three countries that constitute the North American Free Trade Agreement (NAFTA), over the period 1997-2011. The study obtained data from the World Bank’s World Development Indicators database for each country’s Money Supply, Gross National Income and Consumer Price Index, as well as Statistics Canada’s CANSIM database. Results were compared to the seminal works of Swamy (1982 and 2009) that tested the Theorem for the Nigerian economy, as well as recent studies by Lazaridis and Livanis (2010) for the Cypriot and Greek economies, and Bauer and Faseruk (2012) for the Canadian economy. For the U.S.A., Canada and Mexico, the Kumara Swamy Theorem of the Inflationary Gap has provided notable explanatory power for the relationship between the growth of the money supply and real G.N.P. The American economy displayed the lowest inflationary gap and the Mexican economy the highest with the Canadian economy in between, but more in line with the American experience. This paper argues that the Theorem is best understood as a long-term average and to disregard short-term fluctuations. Graphical evidence demonstrates that short-term fluctuations in the inflationary gap, either positive or negative, revert to the mean in subsequent years, although the speed of adjustment varies from country to country.
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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 | 0.014 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".