Asymmetric effects of economic policy uncertainty on demand for money in developed countries
Bibliographic record
Abstract
This paper examines the asymmetric effects of economic policy uncertainty (EPU) on the demand for money in Canada, Japan , the United Kingdom, and the United States. We use linear and nonlinear ARDL models with monthly data over the period 1985–2022 to conduct the analysis. Results from the linear ARDL model show that changes in EPU have no short-run or long-run effect on money demand in any country, except in the US, where changes in EPU have a positive short-run effect. However, with the nonlinear ARDL model, we find evidence of short-run and long-run effects across all four countries. Both increases and decreases in EPU have negative long-run effects on Canadian and UK money demand, but a positive effect on US money demand. For Japan, rising EPU has a positive effect on money demand, whereas falling EPU is insignificant. The long-run results are consistent in each country over time. The recent COVID-19 period had a short-run impact across countries and a long-run effect on the relationship between EPU and money demand in Canada and the UK. In contrast, the Brexit period had no differential long-run impact on money demand across countries, and a short run impact was only observed in the UK. Our results highlight the importance of adopting nonlinear ARDL models instead of linear models to analyze money demand and the need to examine countries separately since the long-run effects of EPU on money demand vary across countries.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".