VECM and Variance Decomposition: An Application to the Consumption-Wealth Ratio
Bibliographic record
Abstract
This study uses a variance decomposition technique, which doesn’t rely on the underlying economic theory, in order to implement a permanent-transitory variance decomposition of the consumption-wealth ratio. We break down the wealth variable into financial assets, tangible assets, and human assets. Using quarterly data over the last six decades, we rely on cointegration analysis as the framework for the study, in order to assess the long-term interrelation between consumption shocks, and those from each of the above mentioned wealth components. Our results indicate that wealth components tend to exhibit permanent shocks, while consumption shocks appear to be transitory. Moreover, the results also indicate a low contemporaneous correlation between shocks in consumption and the ones from financial assets, and also between shocks in consumption and the ones from tangible assets. In addition, the variance decomposition of consumption shocks seems to indicate that, over the time a significantly increasing proportion of consumption shocks is explained by financial assets.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".