Comparative Comparison of Factors Affecting Inflation in OPEC and G7 Countries: a hybrid New Keynesian Phillips Curve Approach
Bibliographic record
Abstract
This study attempts to extract the new keynesian phillips curve for OPEC and G7 countries and compare the factors influencing inflation between the two groups. The study covers the period of 1995-2017 and the econometrics method used to estimate the model is the Bayesian Panelvar. The results showed that the effect of expected prices on inflation for the OPEC and G7 countries in the first period is positive. but, this effect disappears for OPEC countries after a while. But in the G7 countries, not only does it not disappear, but for some countries it becomes more(Japan) and for some it becomes negative(France).The expected price effect has a more lasting effect on inflation in G7 countries.Also, the output gap has a similar effect on inflation in all OPEC countries, and in the first periods it has a negative effect on inflation, and after a while this effect disappears completely. However, this variable has a positive effect on inflation in almost all countries in the G7 countries in the first period. But, gradually, the effect of this variable on inflation in some countries is increasing(Japan and Germany) and some negative(France, Canada, USA and Italy).The effect of the previous period's inflation on the inflation of OPEC and G7 countries is quite similar. Also, the results showed that the citizens of the G7 countries pay more attention to the expected price in forecasting inflation than the OPEC group, and the people of the OPEC member countries consider the inflation of the previous period more.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.002 | 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 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".