Bibliographic record
Abstract
The US federal government has long played a pivotal role in the mortgage market through various agencies, most notably the government-sponsored enterprises (GSEs). The importance of these agencies in the housing credit policy landscape increased during the 1990s and in the years leading up to the Great Recession. This article examines the time-varying effects of monetary policy on mortgage credit, focusing on the role of housing credit policy from the early 1990s to 2014. Using a time-varying parameter vector autoregression model and high-frequency monetary policy surprises, I show that GSEs’ activity in the secondary mortgage market has shaped the response of mortgage originations to monetary policy shocks. As GSEs became more involved in housing policy, the response of mortgage refinancing originations and GSEs’ mortgage purchases to monetary policy strengthened. This suggests that contractionary monetary policy, by undermining housing policy objectives and increasing profit opportunities from mortgage purchases, may prompt a stronger response from GSEs, which in turn dampens the adverse effects of monetary policy tightening on housing activity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".