The Relationship between the Stock Markets and Economic Policy Uncertainty: An Application on Some Developed and Developing Countries
Bibliographic record
Abstract
Factors such as war, crisis, and political election occurring in the global system create uncertainties in national and international markets. Emerging uncertainties have important effects on the decisions to be taken by countries regarding macroeconomic aggregates and may lead countries to uncertainty in terms of economic policy. In an environment of uncertainty, investors' confidence in the economy may decrease and stock markets may be adversely affected by this situation. In this study, it is aimed to examine the relationship between the economic policy uncertainty and stock markets. Accordingly, a study was conducted using panel causality analysis and monthly data covering the period from August 2010 to February 2022 in some developed and developing countries (USA, Australia, Belgium, Brazil, China, India, Hong Kong, England, Ireland, Japan, Canada, Mexico, Pakistan, Russia, Chile). Panel causality analysis, which considers cross-sectional dependence and heterogeneity, shows that economic policy uncertainty affects the stock market in Japan, the stock market affects the economic policy uncertainty in the USA, Australia, Brazil, England, Ireland, Mexico, Pakistan, and both sectors affect each other in Canada.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".