Bibliographic record
Abstract
Abstract. We develop a dynamic, stochastic, general‐equilibrium (DSGE) model due to Ireland (1997) and others and estimate it for the Canadian economy to analyse the real effects of monetary policy shocks. To generate high and persistent real effects, the model combines nominal frictions in the form of costly price adjustment with real rigidities modelled as convex costs of adjusting capital and/or employment. The structural parameters identifying transmission channels are estimated econometrically using a maximum‐likelihood procedure with a Kalman filter. The estimated nominal and real rigidities impart substantial and persistent real effects following a monetary policy shock. Furthermore, the results suggest that the monetary authority has accommodated technology shocks and has successfully offset the real effects of money‐demand shocks, by actively responding to these shocks. JEL classification: E31, E32, E52 Un modèle d’équilibre général dynamique et stochastique doté de rigidités nominales et réelles et calibré avec des données canadiennes. Dans la présente étude, nous développons un modèle d’équilibre général dynamique et stochastique (EGDS), élaboré par Ireland (1997) et d’autres, et estimons ce modèle pour l’économie canadienne. Afin de générer des effets réels considérables et persistants des chocs monétaires, nous intégrons à ce modèle des frictions nominales et réelles sous la forme de coûts d’ajustement des prix, du capital et de l’emploi. En utilisant la méthode du maximum de vraisemblance et le filtre de Kalman, nous estimons les paramètres structurels du modèle avec des données canadiennes. Les versions du modèle d’EGDS doté de rigidités nominales et réelles génèrent des effets réels significatifs et persistants en réaction à des chocs de politique monétaire. De plus, les résultats suggèrent que l’autorité monétaire réussit bien à contenir les chocs technologiques et à annuler les effets des chocs de la demande de monnaie.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".