EMPIRICALLY EXPLORING THE IMPACT OF INTEREST RATE AND ANNUAL CPI DIFFERENCE ON EXCHANGE RATE
Bibliographic record
Abstract
This paper examines the relationship and the impact of interest rate and CPI difference from one year to another on the exchange rate of the home country. This particular study has been conducted in the context of Pakistan which serves to be the home country, and the empirical findings are made in relation to China, Japan, UK and USA. The study uses the panel data concerning the exchange rate, interest rate and CPI difference for all the five countries ranging from the first quarter of 1991(Q1) to the last quarter of 2011(Q4). The results of the study validate the conjecture of the literature that in the long-run, inflation affects the exchange rate in a positive way, while interest rate prevailing in a country has a negative impact on the exchange rate. The results of the panel data regression on the cumulative data of all the countries, with fixed-effect and random-effect shows that the relationship prevails but both the CPI difference and interest rate affects the exchange rate to a very insignificant level. Comparatively, the results of LSDV, which involved evaluating the coefficients on the country-specific level, shows that interest rate and CPI change has significant impact on the exchange rate.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".