The Causal Effects of Economic Policy Uncertainty on Changes in Exchange Rates and Volatility: Empirical Evidence from Türkiye
Bibliographic record
Abstract
Applying a novel econometric method, nonparametric causality-in-quantiles approach, this paper investigates the causal effects of economic policy uncertainty (EPU) on Turkish changes in exchange rates and volatility with the monthly data spanning from February 1998 to December 2019. This approach of gives an opportunity to investigate the (non)causality in the θ-th quantile only in mean (first moment,i.e., m=1) or variance (second moment,i.e.,m=2) as well as the (non)causality in the mean and variance (m=1 and 2) successively. In sum, this approach calculates volatility by squaring returns. We use EPU indexes of the United States, Australia, European Union, Japan, Canada, and the United Kingdom and their currencies (USD, AUD, EUR, JPY, CAD, GBP, respectively) vis-à-vis Turkish Lira (TRY) and find that the EPUs of Australia, the European Union and Japan affect the returns of the AUD/TRY, EUR/TRY and JPY/TRY exchange rates, respectively. These results show that the EPU indices of these countries can give an idea about the returns and volatility of the relevant Turkish changes in exchange rates. The findings of this paper provide important implications for policymakers, investors, firms, exporters, and importers. Also, some studies can be carried out on the effects of the EPU index that will be created to Türkiye on the Turkish exchange rates or the other Turkish financial assets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".