Comparing Forward Guidance and Neo-Fisherianism as Strategies for Escaping Liquidity Traps
Bibliographic record
Abstract
What path should policy-makers select for the nominal rate when faced with a liquidity trap during which the effective lower bound binds? Conventional wisdom has generally favoured a commitment to keep rates low for long, namely under the guise of forward guidance policies, while Cochrane (2016) and others have recently made the case for neo-Fisherian policies that involve pegging rates at a high level in the hopes that the Fisher effect might deliver higher inflation over time. We compare these two options as strategies for escaping liquidity traps and argue that their relative merits likely depend on the mechanism that initially gave rise to the particular trap in question. More specifically, we argue that policy-makers should distinguish between “shock-based” traps that arise following large, negative demand shocks (Eggertsson and Woodford 2003) and “expectation-based” traps that arise from self-fulfilling shifts in private sector expectations (Benhabib, Schmitt-Grohe and Uribe 2001). This is because forward guidance likely dominates in the former case, while the latter may favour neo-Fisherianism to the extent that keeping rates low for long might reinforce the pessimistic beliefs underlying expectation-based traps. Although empirical strategies for distinguishing between these two mechanisms would be a promising topic for future research, we conclude by arguing that the shock-based mechanism likely provides a more plausible explanation for the low inflation witnessed in many developed countries during and after the Great Recession.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".