Examining the Effects of Economic Policy Uncertainties on the Stock Market Index: Analysis by Nonlinear ARDL Method for G7 Countries
Bibliographic record
Abstract
Uncertainties are important factors that influence the decisions made by societies. Economic uncertainties closely affect society’s consumption and investment behaviour. Rising stock markets increase investors’ confidence, resulting in more purchases and higher stock prices and, in this context, an increase in consumer spending. When stock prices decrease, company investments are also negatively affected as consumer spending declines. Thus, increases and decreases in stock prices affect the general economy as they affect business confidence and consumers. The study analyses the effect of uncertainty in economic policies on stock markets, leading to a decrease in investor confidence in the economy. Such effects in G7 countries were examined using the nonlinear autoregressive distributed lag (ARDL) model for the period 1998:M05–2020:M09. This method was able to capture symmetries and asymmetries in the relationship between economic policy uncertainties and the stock markets. The results showed that heightened uncertainty in economic policy in Japan has a significantly negative effect on the stock market index, but in Germany and Italy, it has a significantly positive effect. Rising interest rates have negatively affected the stock market index in the United States, Canada, Japan, Italy, and the United Kingdom. The increase in the industrial production index is positively related to the stock market index in the United States, Canada, Japan, Italy, and France. Additionally, uncertainties in economic policy have asymmetric impacts on the stock market index in the United States, Canada, Japan and Italy, and symmetrical impacts in Germany, France and the United Kingdom.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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) | 0.003 | 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".