Foreign Exchange and Inflation in Pakistan: Evidence from ARDL Modelling Approach
Bibliographic record
Abstract
Interest in detection of factors consider responsible for uneven fluctuation in steady state growth of world economies is long standing. There has been an explosion of theoretical literature and empirical evidences which think about compassionate to resolve the issue. Hike in prices of goods and services and foreign exchange are two important aspects which believe to blame for such bumpy vacillation in economic growth of the world economies like all other political, social and economic factors. It is true that both factors have inimitable significance for economic growth, but inquiry about internal relationships of above said both variables still has research thirst. The novelty of this research paper is, it provides the empirical evidence regarding the relationships between foreign exchange and inflation focusing on Pakistan experience since 1960. We use the Auto Regressive Distributive Lag Model (ARDL) proposed by Pesaran et al. (2001) in order to investigate the order of co-integration between inflation and foreign exchange through bound testing approach, and also use the OLS estimation to determine the long run relationship. Through econometric techniques, we trace the nature of relationship and speed of adjustment between concerned variables in response to fluctuation in level of foreign exchange. Empirical results indicate the negative correlation between the level of foreign exchange and rate of inflation in Pakistan during study period.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".