How Has the Monetary Transmission Mechanism Evolved Over Time?
Bibliographic record
Abstract
We discuss the evolution in macroeconomic thought on the monetary policy transmission mechanism and present related empirical evidence. The core channels of policy transmission -the neoclassical links between short-term policy interest rates, other asset prices such as long-term interest rates, equity prices, and the exchange rate, and the consequent effects on household and business demand -have remained steady from early policy-oriented models (like the Penn-MIT-SSRC MPS model) to modern dynamic-stochastic-general-equilibrium (DSGE) models. In contrast, non-neoclassical channels, such as credit-based channels, have remained outside the core models. In conjunction with this evolution in theory and modeling, there have been notable changes in policy behavior (with policy more focused on price stability) and in the reduced form correlations of policy interest rates with activity in the United States. Regulatory effects on credit provision have also changed significantly. As a result, we review the empirical evidence on the changes in the effect of monetary policy actions on real activity and inflation and present new evidence, using both a relatively unrestricted factor-augmented vector autoregression (FAVAR) and a DSGE model. Both approaches yield similar results: Monetary policy innovations have a more muted effect on real activity and inflation in recent decades as compared to the effects before 1980. Our analysis suggests that these shifts are accounted for by changes in policy behavior and the effect of these changes on expectations, leaving little role for changes in underlying private-sector behavior (outside shifts related to monetary policy changes).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".