Bibliographic record
Abstract
In this thesis, I contribute to the literature on multiple comparisons and specification testing for multivariate models, through the lens of model selection procedures in asset pricing.In the first chapter, I provide a model selection procedure for multivariate models, generalizing the model confidence set (MCS) procedure to systems of N > 1 dependent variables.A (1 -α) level MCS collects the set of models with equal predictive ability, based on a sequential elimination procedure that relies on an equivalence test.I introduce supremum-type t and Hotelling-type T 2 statistics which account for correlation between loss differentials.I assess the performance of 14 candidate asset pricing models using monthly data for the period 1972-2013.I find that for out-of-sample tests, only a single model is ever selected by the procedure, but the MCS often includes multiple models for in-sample tests.Overall, out-of-sample tests and a larger number of more heterogenous test assets provide more information to disentangle models.The procedure shows good size and power properties in simulations. I am grateful to my thesis supervisor, Lynda Khalaf
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".