Trend Analysis of Indian Foreign Exchange Reserves after Post COVID-19 Pandemic
Bibliographic record
Abstract
India is one of the leading countries for economic growth worldwide, and external trade showed a positive trend in the last quarter of the year. According to the Ministry of Economy and Finance (annual report 2021), there has been a strong correlation between capital flow and the positive growth of FOREX reserves as compared with Asian countries. It is the fourth-largest forex reserve holder in the world as of December 2022. India’s merchandise exports and imports showed a linear relationship and declined during the COVID-19 pandemic hit due to the financial burden, low parity of purchasing power, unemployment, low production performance in the manufacturing companies, higher debt, improper management of the service sector, etc. According to RBI statistics, foreign exchange reserves hovered at US$63.10 billion in the first half of last year. Financial inflation is a scourge in many parts of the world. A necessary analytical study will be necessary for taking the right decision at the right time to control financial inflation at the global level. In this paradigm, the present study will attempt to address the trend of forex and GDP by applying advanced statistical modeling techniques and revisiting financial principles to correlate with real data sets for predicting economic feasibility by 2030. This study will help economists and financial analysts initiate operational research on an empirical basis and also greatly assist in drafting financial policy at the national and global level.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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; 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".