Bibliographic record
Abstract
Following Campbell (1987) and Campbell and Shiller (1987), many papers have evaluated the intertemporal approach to the current account by testing restrictions on a Vector Autoregression (VAR). The attractiveness of the Campbell-Shiller methodology is that it is thought to be immune to omitted information. This paper uses results from Hansen and Sargent (1991a) and Quah (1990) to show that this is not true in certain (empirically plausible) situations. In particular, it is shown that if fundamentals are driven by unobserved (to the econometrician) permanent and transitory components, then the theoretical restrictions of a standard Present Value model of the current account might not be testable with a VAR. This is because the theoretical moving average representation can turn out to be noninvertible. This implies that observed data, including the current account, do not reveal the underlying shocks to agents’ information sets. ; These results are potentially relevant given the results of several recent papers which claim that current accounts are ‘excessively volatile’. I provide a simple example in which a researcher employing the Campbell-Shiller methodology is tricked into thinking the current account responds excessively to shocks when in fact the data are consistent with the theory.
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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 | 0.074 | 0.427 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".