Conceptual Perspectives on Selecting the Principal Variables in the Purchasing Managers' Index
Bibliographic record
Abstract
The current research investigates the choice of principal variables for computing the Purchasing Managers' Index (PMI). To this end, the principal components variable selection strategy considered by Jolliffee (1972, 1973) is applied to monthly data on five key diffusion indexes for the period from January 1948 to October 2004, compiled by the Institute for Supply Management (ISM). Results do not support the ISM's current practice of assigning different weights to the five diffusion indexes (i.e., the highest weight to the new orders diffusion index and the lowest one to the inventory diffusion index). Findings also support that a simpler PMI based solely on the employment diffusion index, one of the five key PMI indicators, can be computed without loss of too much information. In many cases, the PMI series offered in this paper outperforms the PMI series proposed by others.
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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 | 0.028 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".