Re-exploring the CCAPM: The Case of US Industry Returns with Different Price Deflators
Bibliographic record
Abstract
Extending US samples, this paper re-examines the classic consumption-based capital asset pricing model (CCAPM) by the generalized method of moments (GMM). Our re-exploration using US three industry returns and different price deflators supplies the following evidence. First, 1) regarding the CCAPM using the US consumption for nondurable goods and the deflator of total personal consumption expenditures (PCEs), the discount rate and risk aversion parameters show plausible values; and according to the J-tests, our above first CCAPM is generally supported. Second, 2) as for the CCAPM with the US consumption for nondurable goods and services and the deflator of total PCEs, both discount rate and risk aversion parameters generally exhibit plausible values and our J-tests show that our above second CCAPM is highly supported. Third, 3) as for the CCAPM using the US consumption for nondurable goods and the deflator of the PCEs for nondurable goods, both parameters of the discount rate and risk aversion are highly stable and our J-tests indicate that our above third CCAPM is highly supported. Finally, 4) as regards the CCAPM using the US consumption for nondurable goods and services and the calculated implicit deflator of the PCEs for nondurable goods and services, the parameters of the discount rate generally exhibit plausible values, while the risk aversion parameters are not so stable. However, according to the J-tests, our above fourth CCAPM is also highly supported.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".