An Empirical Analysis of Foreign Exchange Reserves in Emerging Asia,” Bank of Canada, Working Paper
Bibliographic record
Abstract
ver the last few years, the U.S. ability to finance its current account deficit has been facilitated by massive pur-chases of U.S. Treasury Bonds and agency securities by Asian central banks. As a re-sult, Asian central banks have accumulated large stockpiles of U.S.-dollar foreign exchange reserves. In theory, a country holds reserves as a buffer stock to smooth unexpected and temporary im-balances in international payments. In deter-mining the optimal level of reserves, the monetary authority will seek to balance the costs of macroeconomic adjustment incurred if reserves are exhausted with the cost of holding reserves. Reserve hoarding entails sterilization costs stemming from the negative spread be-tween the interest earned on reserves and the in-terest paid on the country’s public debt. Moreover, if capital flows are not sterilized, sus-tained accumulation of reserves will, at some point, generate inflationary pressures that could threaten domestic financial stability. If Asian central banks decide to stop accumulating U.S.-dollar reserves, they could trigger an abrupt de-preciation of the U.S. dollar. Given the potential impact on global interest rates, economic growth, and financial stability, the issue of Asian reserve accumulation is of considerable importance. Our objective is to assess the degree to which the current level of foreign exchange reserves held by Asian central banks diverges from that predicted by the standard macroeconomic de-terminants.1 To do so, we estimate a long-run demand function for reserves in a panel of eight
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".