The impacts of the COVID-19 pandemic on the Portuguese economy : a structural break analysis
Bibliographic record
Abstract
The COVID-19 pandemic could led to one of the worst crises in history (Barro, 2020). It would be the second crisis in the 21st century the Portuguese economy encounters but it is still unknown if its effects would be structural (permanent and related to the supply side) or cyclical (transitory and related to business cycle fluctuations and the demand side). To test for structural change, a linear regression model is used and the Chow test and Chow test for predictive failure are applied. The model uses quarterly data from various sources, ranging from the first quarter of 1998 to the third quarter of 2020, and is estimated in seasonal differences. It considers as dependent variable the natural logarithm of output per worker and as explanatory variables the natural logarithm of physical capital per worker, a time trend, and other variables on the supply and demand side as controls. A Chow test was first applied to the 2008 Global Financial Crisis, to rule out any interferences from this period. A structural break was found in the fourth quarter of 2010. Applying a Chow test for predictive failure to a subsample from the first quarter of 2011 to the third quarter of 2020, the null hypothesis of the pandemic not causing a structural break was rejected. T-tests were used to confirm the location of the breaks. The null hypothesis that a break did not occur was rejected for all the coefficients of the regression variables, pointing for the existence of supply side permanent effects.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".