Output Comovement and Inflation Dynamics in a Two-Sector Model with Durable Goods: The Role of Sticky Information and Heterogeneous Factor Markets
Bibliographic record
Abstract
In a simple two-sector New Keynesian model, sticky prices generate a counterfactual negative comovement between the output of durable and nondurable goods following a monetary policy shock. We show that heterogeneous factor markets allow any combination of strictly positive price stickiness to generate positive output comovement. Even if the prices of durable goods are flexible, adding sticky information ensures that the output of both sectors moves in the same direction. Furthermore, we find that the combination of sticky information and heterogeneous factor markets produces hump-shaped responses in both sectoral output and inflation, as observed in a vector-autoregression analysis. In contrast to backward indexation to past inflation, which is often assumed in the literature, sticky information leads to a hump-shaped response in the inflation of flexibly priced goods. Finally, the estimated information stickiness through the minimum-distance estimation method suggests that information rigidity is stronger in residential investment than nondurable goods and services.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".