Evidence from the European Union 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 27 European Union countries over the period 1999-2011. The study obtained data from the World Bank’s World Development Indicators database for each country’s money supply, Gross National Income (GNI) and Consumer Price Index (CPI), as well as the Bank of England for the United Kingdom and the comparable agencies in Norway and Sweden. Results were compared to the seminal works of Swamy (1982) and (2009) that tested the theorem on the Nigerian economy, as well as recent studies by Lazaridis and Livanis (2010) for the Cypriot and Greek economies, Bauer and Faseruk (2012) for the Canadian economy and Faseruk, Bauer and Glew (2012) for the countries in the North American Free Trade Agreement. In the current study, 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 GNP in terms of direction but not necessarily in terms of magnitude. In order to explain differences, this paper examines subdivided samples for various sub-groupings, such as Portugal, Ireland, Italy, Greece and Spain, as well as former COMECON countries. This paper agrees with the insight from Faseruk, Bauer and Glew (2012) that the theorem is best understood as a long-term average and to disregard short-term fluctuations. Graphical evidence demonstrates when short-term fluctuations in the inflationary gap occurred and provides various explanations based on market data.
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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 | 0.035 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".