Has the Asymmetric Effect of Oil Price Change in Inflation Expectations Been Impacted by the COVID-19 Outbreak? A Comparison Between the United States and China
Bibliographic record
Abstract
Economists and policymakers believe that households’ and firms’ expectations of future inflation are key determinants of actual inflation. This paper applies the ARDL model and nonlinear ARDL model to long-term inflation-targeting policy mechanisms in the United States and China to assess the impact of oil price dynamics and asymmetries on inflation expectations, as well as the difference of this impact before and after the COVID-19 pandemic. In order to show the significant role of the COVID-19 outbreak, this paper includes the data from 2010 to 2021 and takes the pandemic period as a structural break. Taking oil price changes as a variable of interest, and introducing some other significant variables, we find that during the pandemic, the positive impact of oil price shock on U.S. inflation expectations has enhanced, whereas the positive impact on Chinese inflation expectations has weakened. There is also sufficient evidence of the existence of the asymmetric effects of oil price changes on inflation expectations in both countries, but the positive oil price change in the United States has always played a larger role than the negative oil price shock. In China, the impact of positive oil price shock was greater than that of negative oil prices before the epidemic and the effect of negative oil price shocks has increased significantly in the COVID-19 regime.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".