Interest Rate Interactions between Bangladesh and the US: Possible Pass Through From the US
Bibliographic record
Abstract
This paper investigates the long run relationship between the interest rates of Bangladesh and the United Sates (US). Using time series quarterly data for the period 1972- 2019, the study finds that the nominal rate of the US positively influences the nominal rate of Bangladesh and they do maintain a long run relationship. Similar result is obtained by examining the real rates of both countries. However, in the latter case the study period covers from the third quarter of 1993 to the third quarter of 2019. Estimation of the error correction model signifies that in both cases policy rate of Bangladesh significantly responds to the error, which is the measure of deviation from long run equilibrium. Although interest rates of Bangladesh respond to the error in both cases, the speed of adjustment is much higher in case of the real rates. Empirical findings reveal that around 6% error is corrected in every quarter if it is nominal rate whereas in the event of real rate the rate of error correction is almost 77%. These findings indicate that small economy Bangladesh plans its policy rate taking account of the dynamics of the large economy the US, and such policy dependence is more apparent for real rate of interest.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".