The Effect of External Economic Shocks on Iran's Macroeconomic Variables: Global VAR Approach
Bibliographic record
Abstract
Macroeconomic policy analysis and risk management require taking account of the increasing interdependencies across markets and economies. National economic issues need to be considered from global as well as domestic perspectives. This invariably means that many different channels of transmission must be taken into account. This paper investigates the effect of global economic shocks on Iran’s economy. The Global Vector Autoregressive (GVAR) model for the first quarter of 1990 to the fourth quarter of 2019 is used for 34 countries, which cover about 90% of world gross domestic products. According to previous studies and the results of this study, it is found that only the shocks of the United States, China and the global shock affect the macroeconomic variables of other countries and oil prices, and as a result, the effect of these three shocks on the Iranian economy is investigated. Ceteris paribus, the results show that China's shock affects the variables of GDP and Iran's inflation: with a 1 percent increase in China's GDP, Iran's GDP increases by 0.08 percent and inflation by 1.2 percent and has no effect on interest rates. The US shock has an indirect effect on oil prices. Due to the isolation of the economy, foreign variables do not have significant effects on the Iranian macroeconomic variables. In general, Iran's economy, due to the size of the economy and the volume of trade shocks of other trading partners through the foreign trade channel do not affect the Iranian 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".