Do Oil Prices have an Asymmetric Effect on Economic Growth?: Evidence from the NARDL Approach for Korea, China, and Japan
Bibliographic record
Abstract
In this paper, We empirically analysis the impact of oil prices on the economic growth of Korea, China, and Japan in Northeast Asia. The analysis period used variables such as per capita GDP, international oil prices, real effective exchange rates, consumer price index, and total currency in the three countries from the first quarter of 2000 to the fourth quarter of 2022. The NARDL model proposed by Shin et al. (2014) was applied to analyze the effects of rising and falling oil prices. In this study, first, it was found that fluctuations in oil prices in all three countries affect economic growth. However, it was not statistically significant in the long run. Second, the impact of oil prices on economic growth in all three countries was found to be asymmetric. In the short term, the rise and fall of oil prices showed statistical significance in Korea, China and Japan. Third, the impact of oil prices in Korea and Japan is similar to the analysis results of previous studies analyzed in advanced countries (e.g., OECD countries). However, in the short term, the impact of oil price fluctuations on China has differentiated results from Japan and Korea. Each of the three countries’ error correction terms coefficients is statistically significant, which means that short-term economic growth is adjusted to a long-term equilibrium relationship at a certain rate for one year if any impact occurs in the system.
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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".