Long Run Relationship between Selected Macroeconomic Indicators and Banking Sector in Pakistan
Bibliographic record
Abstract
The study investigated the long run relationship between selected macroeconomic indicators and banking sector index in Pakistan. The selected macroeconomic indicators are Exports, Industrial Production, CPI, and KIBOR as short-term interest rate, Money Supply (M0), Nominal Exchange Rate between Pakistan and United States of America (USA), Oil Prices and the Interest rate on Pakistan Government bond ten years, as the long-term interest rate. Monthly time series was used from January 2009 to August 2015. The study applied Augmented Dickey-Fuller test to determine the stationarity levels for the selected macroeconomic indicators and banking sector index, Phillips-Perron test to validate the results of Augmented Dickey-Fuller test, a bound testing technique in ARDL model to investigate the long run relationship between selected macroeconomic variables and banking sector index. Results suggested the presence of a long-run relationship between macroeconomic variables exchange rate, inflation, oil price and banking sector index in Pakistan. Results of Granger causality test suggested unidirectional causality running from macroeconomic variables KIBOR and oil prices to banking sector index in Pakistan. Further, unidirectional causality was found running from banking sector index to government bond in Pakistan.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".