Bibliographic record
Abstract
This paper tests between fads and bubbles using a new empirical strategy (based on switching-regression econometrics) for distinguishing between competing asset-pricing models. By extending the Blanchard and Watson (1982) model, we show how stochastic bubbles can lead to regime-switching in stock market returns. By incorporating state-dependent heteroscedasticity into the Cutler, Poterba, and Summers (1991) fads model, we show that it can also lead to regime-switching. Two main features of the bubbles model distinguish it from the fads model. First, the bubbles model implies that returns are drawn from two distinct regimes. Second, the bubbles model implies that deviations from fundamental price will help predict regime switches. Using U.S. data for 1926-89, we find evidence that is consistent with the fads model even when we allow for variation in expected dividend growth rates and expected discount rates. However, the restrictions that the fads model implies for a more general switching model are rejected. The rejections point in the direction of the bubbles model, although not all the implications of the bubbles model are supported by the data.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".