The Indian Foreign Exchange Market and the Equilibrium Real Exchange Rate of the Rupee
Bibliographic record
Abstract
This article seeks to analyze changes in the forex market in India and to explain the behaviour of the rupee in the nineties when India moved from a fixed to a f loating exchange rate. The analysis attempts to identify the underlying economic forces that are submerged under an interventionist market structure. The exchange rate is determined in the more slowly adjusting output markets in the long run and in volatile asset markets in the short run. The long run equilibrium exchange rate is seen to be determined by a version of the purchasing power parity condition. In the short run, the real exchange rate deviates from that determined by real interest parity due to risk. As long as pressures are not extreme, the Reserve Bank of India (RBI) is able to intervene effectively to influence the exchange rate. Its policy of 'keeping the rupee in line with fundamentals' has usually amounted to preventing a nominal appreciation of the rupee so that it does not effectively turn into a real appreciation. Other than attempting to keep exports competitive, the Reserve Bank has intervened to reduce volatility in forex markets. More recently, indirect intervention using interest rates has also played an increasingly important role in the RBI's foreign exchange policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".