Evaluating Foreign Exchange Market Intervention: Self-Selection, Counterfactuals and Average Treatment Effects
Bibliographic record
Abstract
Studies of central bank intervention are complicated by the fact that we typically observe intervention only during periods of turbulent exchange markets. Furthermore, entering the market during these particular periods is a conscious self-selection choice made by the intervening central bank. We estimate the counterfactual exchange rate movements that allow us to determine what would have occurred in the absence of intervention and we introduce the method of propensity score matching to the intervention literature in order to estimate the average treatment effect (ATE) of intervention. Specifically, we estimate the ATE for daily Bank of Japan intervention over the January 1999 to March 2004 period. This sample encompasses a remarkable variation in intervention frequencies as well as unprecedented frequent intervention towards the latter part of the period. We find that the effects of intervention vary dramatically and inversely with the frequency of intervention: Intervention is effective over the 1999 to 2002 period, ineffective during 2003 and counterproductive during the first quarter of 2004.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".