Threshold Cointegration and Granger Causality Between CPI And PPI In Selected Countries
Bibliographic record
Abstract
This paper mainly investigates the threshold cointegration and Granger-causality relationships between the CPI and PPI series in the selected countries for policymakers to effectively control inflation. We first applied the unit root test to ensure the integration order of all the series, and then both the linear Engle-Granger (E-G) and the nonlinear Enders-Siklos (E-S) cointegration tests for comparative analysis. Lastly, Granger causality tests are adopted in the momentum threshold vector error correction model (M-TVECM), which is used to estimate the different speeds of adjustment and explore the causal relationship between CPI and PPI in the selected countries. While the E-G test cannot detect cointegration in almost all countries, the E-S test with higher power when there is asymmetric adjustment, supports the cointegration relationship in Canada, Denmark, Indonesia, Japan, Pakistan, Spain, and Uruguay. The evidence also supports the existence of asymmetric threshold adjustment in all cointegrated systems. In addition, the empirical results indicate that Granger causality in the M-TVECM can be classified into two categories. One kind is about CPI leading to PPI, including Spain only while another kind is about bidirectional causality between CPI and PPI for other countries in the M-TVECM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".