The Implications of Sectoral Heterogeneity for Monetary Policy and Welfare in a Small Open Economy: A Linear Quadratic Framework
Bibliographic record
Abstract
Modern economies exhibit various structural and dynamic characteristics. At the same time, many central banks have implemented the similar strategy, i.e. in-‡ation targeting, as an operational framework. Controversial normative issue- is such stabilization objective welfare maximizing for more complex models with heterogeneous elements across sectors? This article analyzes optimal monetary strategy and policy trade-o¤s in a DSGE model of an open economy with traded and nontraded sectors. We approximate the utility of the representative consumer to obtain a micro-founded quadratic loss function of the form extensively used for monetary policy assessment. The central bank’s optimal strategy is computed and optimal and simple policy rules compared according to the derived welfare measure. We assess the role of openness, structural characteristics, and relative prices for monetary policy design. The model is calibrated to match the moments of main macroeconomics variables of Canadian economy. The …ndings suggest that social welfare objectives display sector-speci…c features thus generating important implications for optimal policy and welfare. The analysis of the performance of simple rules indicates that ‡exible CPI targeting regime that includes a certain degree of internal relative prices management is able to closely replicate the optimal solution. Finally, we assess the implications of sectoral heterogeneity in price stickiness for bene…ts of targeting the core versus broader price indices.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".