CRISIS AND CURE: COMPARATIVE POLICY RESPONSE TO ECONOMIC FLUCTUATIONS IN ADVANCED SELECTED ECONOMIES
Bibliographic record
Abstract
Economic fluctuations can hinder the economic growth of any country while making an economy vulnerable. This paper attempts to analyse the economic fluctuations in four major developed countries of the world including Canada, France, United Kingdom and United States of America. Augmented Dickey Fuller (ADF) test was applied to check the stationarity of GDP series for all the four economies. All the GDP series were found to be integrated of order one. Cyclical components of each GDP series were separated and recovered by using Hodrick- Prescott (HP) filter while showing downturn and recoveries in these economies. As HP filter also soothes out the series so all the cyclical components turn stationary. In order to understand the interaction between USA and Canadian economy Granger Causality test was applied. USA GDP is important to forecast Canadian GDP with all 1, 2 and 3 lags. While Canadian GDP was useful to forecast USA GDP only in case of lag 1. Regarding growth evolution of these four countries during various recessions Canada and France experienced relatively fewer recessions as compared to UK and USA while it seems that UK experienced longest recession and suffered most from the recession. Role of monetary and fiscal policies as crisis response is also discussed for these economies. It is found that USA and Canada have almost fully recovered from the recession but UK and France had to face longer recovery periods. These different recovery patterns might be attributed to different policy responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".