Statistical and Econometric Analysis of Selected Effects of COVID-19 Pandemic
Bibliographic record
Abstract
Abstract The paper examines the impact of the COVID-19 pandemic on macroeconomic activity in the selected European countries. The studies are based on monthly and quarterly indicators of GDP, unemployment rates and key indicators of the tourism sector. To present how COVID-19 has affected these macroeconomic variables, statistic data from the three periods are compared. Namely, data are collected from the pre-pandemic period, i.e. the fourth quarter of 2019 as the reference period, the second period covers the first quarter of 2020 and means the beginning of the pandemic, and the third one covers second quarter of 2020, during which the pandemic has spread to all the analyzed countries. The following statistical techniques are used in the research: regression analysis, the hierarchical grouping of agglomerations, k-means method, and selected non-parametric tests (Kruskal-Wallis test for a selected group of countries and Kolmogorov-Smirnov test for a selected pair of countries). The results show the significant impact of the pandemic on the level of gross domestic product, unemployment rate and turism sector. In most cases, a correlation between incidence of COVID-19 infections, unemployment rate and GDP is observed. The statistical techniques also allow to demonstrate the similarities and differences in the response of the economies to the COVID-19 pandemic. Central Statistical Offices of the selected countries are the main data source and for all calculations Statistica version 13.3. is used.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".