Firm Entry and the Monetary Transmission Mechanism — preliminary and incomplete — –
Bibliographic record
Abstract
This paper estimates a business cycle model with endogenous …rm and product entry. We evaluate how the extensive margin a¤ects structural parameters and the relative importance of di¤erent frictions in the monetary transmission mechanism. To this end, we minimise the distance between the impulse responses of selected US variables to a monetary policy shock in the model and in an identi…ed vector autoregression. Our VAR contains net …rm entry in addition to the usual macroeconomic aggregates. The proposed model, which assumes the same type of adjustment costs for entry as for capital investment, does a good job at matching the observed dynamics. Price setting frictions are estimated to be small, while wage rigidities play an important role. Investment adjustment costs are greater than an estimated model without entry would suggest. The data prefer a version of the model in which the variety e¤ect is absent. Key words: monetary transmission, monetary policy, entry, extensive margin JEL codes: E32, E52 Thanks to Martina Cecioni, Gert Peersman and Raf Wouters for very useful comments. We also are grateful to participants of the Canadian Economic Association Meeting, 2010. The views expressed in this paper do not necessarily re‡ect those of the National Bank of Belgium. All remaining errors are the authors’. y
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".