Small-Country Mundell–Fleming (IS/LM/BP) Model Predictions Under Both Fixed and Flexible Exchange Rates: Evidence from Australia and S. Korea
Bibliographic record
Abstract
The small-country IS/LM/BP (Mundell–Fleming) model predicts that monetary policy is totally ineffective in countries with fixed exchange rates and super-effective in countries with flexible exchange rates. Furthermore, this model predicts that, under fixed exchange rates, fiscal policy is stronger the more mobile capital is in terms of moving in and out of the country, but it predicts the opposite for countries with flexible exchange rates; fiscal policy is stronger the less mobile capital is. This paper tests these predictions by applying reiterative truncated projected least squares (RTPLS) to quarterly data from Australia and the Republic of Korea when they employed fixed and then flexible exchange rates. RTPLS produces a separate total derivative estimate for each observation, where the differences in these estimates are due to omitted variables. By doing so, RTPLS makes it possible to see how the estimated relationship changes over time. I found that the effectiveness of monetary policy was not zero under fixed exchange rates but that its effectiveness did increase when Australia and S. Korea switched to flexible exchange rates. Under flexible exchange rates, I found that the effectiveness of fiscal policy was statistically higher than zero for both countries, which conflicts with the assumption of perfect capital mobility.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".