Trade Policy Uncertainty Effects on Macro Economy and Financial Markets: An Integrated Survey and Empirical Investigation
Bibliographic record
Abstract
This paper conducts a review on theoretical and empirical findings on the increasingly popular measure of trade policy uncertainty (TPU) in economics and finance. Moreover, an empirical investigation takes place in order to find the impact that TPU exerts on Bitcoin market values by employing a spectrum of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) specifications. Existing studies support that trade policy uncertainty leads to lower-quality and more expensive products and weak participation in international trade. Moreover, it contributes to lower democratic sentiment, hesitant internal migration and lesser socio-economic mobility and higher fluctuations in profitable assets. Moreover, our econometric findings reveal that TPU positively affects Bitcoin prices while crude oil values negatively influence this major cryptocurrency. Thereby, higher trade policy uncertainty is found to increase demand and favorite investments into risky assets in order to ameliorate the risk-return trade-off in investors’ portfolios. This study provides a compass for investing during turmoil due to trade wars and tariffs.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".