Occasionally Binding Constraints in Large Models: A Review of Solution Methods
Bibliographic record
Abstract
"Policy-makers use macroeconomic models to forecast the economy, analyze important events and assess the impact of key public (macroeconomic) policy actions. Detailed microeconomic featuressuch as decisions and interactions of firms and households—provide a rich theoretical foundation for the analysis. Solving complex models is difficult, and analysts often approximate the model by linearizing the equations. Linearization makes the model easier to solve. But the benefits come at the loss of important details of the behaviour of people and businesses in the model economy. A common type of feature lost in this approximation is occasionally binding constraints (OBCs). Perhaps the best-known example of an OBC is the zero lower bound on nominal interest rates—the presence of a point beyond which the central bank would not further cut interest rates. This paper considers various methods available to use with a linearized model to account for OBCs. Such accounting restores the important features associated with the model design. I show that all methods are broadly comparable, but dynareOBC (a tool kit used with the software Dynare) is faster and can be more accurate."
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".