Estimating the Impact of Covid-19 Pandemic on European Countries GDP Somaya Shorafa
Bibliographic record
Abstract
This master thesis aims at estimating the economic cost of the covid-19 pandemic in terms of the GDP loss for 31 European countries in 2020 and 2021. I did simulations using an ARIMA model based on the real GDP time series (1983-2021). The forecast results showed that real GDP in the second quarter of 2020 had the deepest recession (-12.81 %). As the restrictions was lifted, the GDP largely recovered by 9.01% in the third quarter of 2020. The total real GDP level was below its predicted level in all countries except Ireland (3.25%). In 2021, there was a 2.32% and 0.01% drop in the real GDP over the first and second quarters. The Real GDP growth rate was positive for Bulgaria, Estonia, Ireland, Latvia, Lithuania, Luxembourg, New Zealand and Turkey in all 2021 quarters. The difference between the real GDP and the predicted GDP showed that the Lost Economic output was 6.5 trillion U.S. dollars in 2020 and 651 billion U.S. dollars in 2021.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".