RECHERCHE DE LA DYNAMIQUE PROPRE DE VARIABLES MONETAIRES DANS L'ECONOMIE MAROCAINE / A RESEARCH ON THE DYNAMICS OF MONETARY VARIABLES IN THE MOROCCAN ECONOMY
Bibliographic record
Abstract
Morocco is supposed to have enforced a stabilization policy since 1983 which would affect a fairly large number of economic variables. In this connection, five variables are being studied by the Author, viz. the monetary base, money balances and money supply, as well as credit to the economy and interest rates on the money market. Contrary to existing studies in this field, data are collected on a quarterly basis and treated through dynamic analysis. The Author uses the so-called Box & Jenkins's method to study the dynamics of each variable (G.E.P. Box and J.M. Jenkins, 1976, Time Series Analysis Forecasting and Control, Holden-Day). The method is one of the best tools to control process and fore casts. In the paper, forecasts are calculated over a four-quarter horizon where obser ved series can be compared with the forecasts. This also allows the identification of the impact of exogenous shocks, provided, of course, that the model is well specified. In this study, shocks showed up in the monetary base and in the money balances, and even more marked in the money market rates. Moreover, it is worth mentioning that, as far as credits to the economy were concerned, while the stabilization programme envisaged that thay would be stabilized after 1983, the data observed for 1985 were higher than the model forecasts.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".