Forecasting exchange rate based on macroeconomic variables: a GVAR approach
Bibliographic record
Abstract
In this thesis, we conduct this study to forecast real effective exchange rate based on macroeconomic variables such as unemployment rate, real interest rate, current share price, industrial production, terms of trade and terms spread along with a global variable real commodity price index. We employ Global Vector Autoregression model (GVAR) developed by Pesaran (2004) and analyse the global impact of the country specific domestic variables on various exchange rates. Our empirical analysis with the help of impulse response reveals that positive shock to the UK and US share price has significant impact on Canadian dollar. Furthermore, a positive shock to the real commodity price index reflects some striking result, narrating that it has significant impact not only on the exchange rate but also on the real interest rate,terms of trade and industrial production. Using newly assembled data and hitherto applied methodologies; we believe that this study imparts valuable insights into the role of the macroeconomic variable on the foreign exchange rate market.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".