GMM Tests of Stochastic Discount Factor Models with Useless Factors
Bibliographic record
Abstract
This paper studies generalized method of moments tests for the stochastic discount factor representation of asset pricing models when one of the proposed factors is in fact useless, defined as being independent of the asset returns. A nalytic results on asymptotic distributions and simulation results on finite sample distributions both show that (i) the Wald test tends to overreject the hypothesis of a zero factor premium for a useless factor when the model is misspecified, (ii) with the presence of a useless factor, the power of the over-identifying restriction test in rejecting misspecified models is reduced, and in some cases a misspecified model with a useless factor is more likely to be accepted than the true model. JEL Classification: G12 Keywords: Stochastic discount factor models; Generalized method of moments; Nonidentifiability; Useless factors; Misspecification * Corresponding author. Tel.: 852/2358-7684; fax: 852/2358-1749; e-mail: czhang@ust.hk. 1 We would like to thank Burton Hollifield, Gu ofu Zhou, seminar participants at Hong Kong University and an anonymous referee for their helpful comments and suggestions. All remaining errors are ours. Kan gratefully acknowledges financial support from the Social Sciences and Humanities Research Council of Canada. 0304-405X/99/$19.00 c #1999 Elsevier Science S.A. All rights reserved 1.
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 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 | 0.014 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".