Economic Policy Uncertainty and Exchange Market Pressure: Panel Evidence
Bibliographic record
Abstract
Controlling for macroeconomic indicators and trade openness, this study examined the impact of economic policy uncertainty on exchange market pressure for a panel of 25 countries from 2003Q2 to 2021Q3. The pooled mean group estimator, which allows for variation in short-run estimates and error variances but constrains long-run parameters to be the same, was employed to conduct the analysis. The overall panel was further split into developed, developing, and emerging economies panels to check if there was variation in the effect of economic policy uncertainty. Further, we split the entire sample into pre and post-GFC period to account for potential nonlinearity caused by the structural break (i.e., global financial crisis). Results indicate significant positive effect of economic policy uncertainty on EMP1. Economic policy uncertainty has larger impact on developing and emerging economies EMP1 than their developed counterparts entire sample period and all countries panel all sample periods. For EMP2 economic policy uncertainty has significant effect only for developing and emerging economies entire sample period. Furthermore, the effect of uncertainty in economic policy on EMP1 is larger in pre-GFC period than post-GFC period for all countries and developed economies panel. For developing and emerging economies, post-GFC is larger than pre-GFC period. All the remaining variables have mixed effect on either of the exchange market pressure indexes. .
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".