Bibliographic record
Abstract
There are four chapters in my dissertation. Chapter one gives a brief introduction of the three essays. Chapter two studies the choice of exchange rate regimes in East Asia using a business-cycle approach. My results suggest that countries in East Asia are driven mainly by country-specific shocks, making more rigid exchange rate regimes less desirable. Neither a yen bloc nor a dollar bloc has been identified in East Asia. However, Japan seems more influential to countries such as Korea and Taiwan. An optimum currency area does not seem feasible for East Asia, at least in the short run. Chapter three applies the cointegration and causality analyses to the real effective exchange rates to study the degree of monetary integration in East Asia. I find that the ASEAN and the NIE countries, respectively, have achieved some degree of integration, but not East Asia as a whole. The yen is found to move closely with the NIE currencies. However, neither the yen nor the dollar imposes a dominant driving force on the East Asian currencies. My results suggest that East Asia is not an optimum currency area. Chapter four expands the traditional monetary model of exchange rate determination into a structural VAR model incorporating various capital flows and the balance of trade in addition to the macroeconomic fundamentals. The model is then applied to the Australian dollar (AUD), the Canadian dollar (CAD), and the US dollar (USD) exchange rates over 19802004. I find that capital flows, especially portfolio investments, explain a major portion of the exchange rate fluctuations in the relatively small and open economies such as Australia and Canada in the short-to-medium run. The impacts of capital flows are limited to the US dollar exchange rates. Among the macroeconomic fundamentals, the interest rate plays an important role in exchange rate determination for all three currencies. The results imply that different capital flows do influence exchange rates differently and are important determinants of exchange rates.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".