Avoiding the Pitfalls: Can Regime-Switching Tests Detect Bubbles?
Bibliographic record
Abstract
Work on testing for bubbles has caused much debate, much of which has focussed on methodology. Monte Carlo simulations reported in Evans (1991) showed that standard tests for unit roots and cointegration frequently reject the presence of bubbles even when such bubbles are present by construction. Evans referred to this problem as the pitfall of testing for bubbles. Since Evans' note, new tests for rational speculative bubbles that rely on regime-switching have been proposed. Van Norden and Schaller (1993) and van Norden (1996) use a switching regression to look for a time-varying relationship between returns and deviations from an approximate fundamental price. Hall and Sola (1993) and Funke, Hall and Sola (1994) test whether asset prices seem to switch between explosive growth and stationary behaviour. Our paper reports on Monte Carlo experiments using Evans' data-generating process to gauge the performance of these two kinds of regime-switching tests. The experiments rely heavily on certain new, fast and robust programs developed at the Bank of Canada for the estimation of switching regression models that make Monte Carlo studies of such estimators practical. We find that for some (but not all) parameter values, regime-switching tests have a significant amount of power to detect periodically collapsing bubbles. We also compare and contrast the performance of the two different regime-switching tests.
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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 | 0.078 | 0.483 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".