The Effects of the Frequency and Implementation Lag of Basket Updates on the Canadian CPI
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Bibliographic record
Abstract
Abstract In this article, we examine the effects of different frequencies and implementation months of basket updates on the fixed-basket price index – the Lowe index, through theoretical analysis and empirical simulation using Canadian data from 2000 to 2013. We find that both an increased frequency of basket updates and a faster implementation of these new baskets will reduce substitution bias in the CPI. However, we also find that improvements to the method of accelerating frequency has diminishing marginal returns in practice – as each subsequent increase in the frequency with which the CPI basket is updated has a less pronounced effect; and the ideal link-month when a new basket is implemented is unpredictable, since the impact of the implementation lag depends upon the consistency between short-term price movements and long-term price trends.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it