Bibliographic record
Abstract
This paper introduces search frictions in nancial markets, within a standard quantita-tive monetary model, with the objective of generating greater persistence in the eects of monetary policy shocks. Specically, the paper rst assumes that banks enter into contact with suitable new entrepreneurs-clients via a search-and-matching mechanism similar to that used to study labour markets (Pissarides, 1985). Here, the interest rate on loans implements the sharing of the surplus associated with each bank-entrepreneur match. Second, we assume that the entrepreneurs, who oversee the production of the economy’s output, operate a diminishing-returns-to-scale technology. In this environment, banks have an incentive to use part of any unexpected liq-uidity injection to search for new clients rather than lending it all out to its existing ones, because spreading out a given supply of funds across a wide pool of projects attenuates the eects of diminishing returns to scale. Further, these increased search eorts generate persistent changes in the relative bargaining power of banks and en-trepreneurs, which aects surplus sharing and thus the lending rate that implements it. The combination of these two eects is shown to add persistence to the decrease in nominal interest rates that follows monetary policy easings and to create hump-shaped responses in output and inflation. JEL classication: E4, E5 We thank Christian Calmes, Merwan Engineer, Allen Head, Peter Ireland, Cesaire Meh and Shouyong Shi for useful discussions and suggestions. We also benetted from the comments of participants at the 2002 annual meeting of the Canadian Economics Association as well as the 2003 meeting of the European Economics Association, and seminar participants at Laval University, Simon Fraser University, and the University of Victoria. The views expressed in this paper are those of the authors. No responsibility for them should be attributed to the Bank of Canada.
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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 | 0.012 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".