Effectiveness of Monetary Policy Instruments on Economic Growth in Jordan Using Vector Error Correction Model
Bibliographic record
Abstract
The global financial crisis emphasized the important role of the prudent monetary policy in supporting economic growth through maintaining price stability. The monetary policy operational framework that was designed in 2008 was updated to include more instruments for managing monetary policy learning from the crisis lessons. Several studies analyzed various dimensions related to economic growth in Jordan such as Abdul-Khaliq, Soufan, and Abu Shihab (2013) and Assaf (2014), there were no studies that investigated the effect of monetary policy on economic growth in Jordan, at least recently, however. The study aims at measuring the effect of monetary policy instruments on the performance of Jordanian economy. Using quarterly data covering the period (2005-2015), an econometric model was examined using Vector Error Correction Model to assess the impact of monetary policy instruments on economic growth. The foremost advantage of VECM is that it has a nice interpretation of long-term and short-term equations. The results showed the existence of positive long-term and short-term effects of monetary policy instruments on the growth of real GDP. The model included three monetary policy instruments besides money supply. They are required reserve ratio, rediscount rate and overnight interbank loan rates as independent variables, and the real GDP growth as a dependent variable. The stationarity of the model time series was addressed. In addition, the stability of the model was tested using stability diagnostics tools. The results showed also an existence of inverse relationship between rediscount rate and economic growth in Jordan over both long and short terms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".