Interpreting the United States Consumer Price Index using index mining techniques and its component price indices: 2012 to 2023
Bibliographic record
Abstract
The Consumer Price Index (CPI) aims to measure inflation. The CPI, published monthly by the US and measured by the price index of ‘All items’, is the sum of the products of component price indices and their relative weights. We analysed the CPI and its component price indices between 2012 and 2023. Based on the correlations between the CPI and its 358 component price indices, the CPI well represented the trends of most component price indices throughout the period. The time series regression coefficients of component indices did not necessarily match the relative weights. If measuring volatility with rolling standard deviations of raw values with 19 or more months time windows, the CPI was less volatile than the core CPI. The CPI is a good measure of relative price changes in most of the goods or services. However, some goods and services have prices fluctuating in patterns very different from that of the CPI. Policy implications include initiatives that focus on the management of macroeconomic risks, early policy responses using the leading indicators of inflation, policies to manage inflation propagation over time or across price indices, and strategies to refine inflation-linked payments.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".