Multilateral Adjustment and Exchange Rate Dynamics: The Case of Three Commodity Currencies
Bibliographic record
Abstract
In this paper, we empirically investigate whether multilateral adjustment to large U.S. external imbalances can help explain movements in the bilateral exchange rates of three commodity currencies – the Australian, Canadian and New Zealand (ACNZ) dollars. To examine the relationship between exchange rates and multilateral adjustment, we develop a new regimeswitching model that augments a standard Markov-switching framework with a threshold variable. This enables us to model the exchange rate dynamics of our commodity currencies in the context of two regimes: one in which multilateral adjustment to large U.S. external imbalances is an important factor driving the commodity currencies and the second in which there are no significant U.S. external imbalances and hence multilateral adjustment is not a factor. We compare the performance of this model, both in and out-of-sample, to several other alternative models. In addition to developing this new model, another distinguishing feature of our paper is that we estimate all of our models using a Bayesian approach. We opt for a Bayesian approach in this context because it provides a simpler and more intuitive means of evaluating and comparing our different non-nested models. Moreover, it is relatively straightforward using a Bayesian approach to evaluate the importance of nonlinearities in the relationship between exchange rates and multilateral adjustment. Our findings suggest that during periods of large U.S. imbalances, fiscal and external, an exchange rate model for the ACNZ dollars should allow for multilateral adjustment effects. Moreover, we also find evidence to suggest that the adjustment of exchange rates to multilateral adjustment factors is best modelled as a non-linear process.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".