Leçons macroéconomiques de la Covid-19: une analyse pour la RDC
Bibliographic record
Abstract
This work aims at studying the macroeconomic impact of COVID-19 on the activity economic in DR Congo. To do this, a dynamic and stochastic general equilibrium model in the open economy is used and the model parameters are estimated using the Bayesian approach. The estimated data cover the period from the first quarter 2012 in the second quarter of 2020. The diagnostic tests, in particular the convergence test Monte-Carlo Markov chains (MCMC) lead to consider that the parameters are reliable. The results indicate that: (i) the COVID-19 shock would lead to a significant drop in the output gap until the 8th trimester after the incurrence of the shock; (ii) the level of consumption also suffers a downside effect following the health crisis up to more than 10 quarters after the shock; (iii) The nominal exchange rate also depreciates with a more and more attenuated from the 6th quarter after the shock, and (iv) the term of exchange suffers also from a negative effect but with a larger confidence interval, which could possibly reflect a potentially significant effect following the interruption of trade resulting from the measures of confinement.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".