Three Essays in Monetary Policy
Bibliographic record
Abstract
This thesis examines critical and timely issues in monetary policy, focusing on balance sheet strategies, forward guidance, and commodity price dynamics. The first essay employs a New Keynesian Dynamic Stochastic General Equilibrium (NK-DSGE) model calibrated to Canadian data to evaluate central bank balance sheet policies during crises marked by simultaneous adverse shocks. Comparing five policy scenarios - including corridor/floor systems, quantitative easing (QE), tightening (QT), and tapering - the findings show that QE during crises followed by QT in recovery optimizes outcomes under pre-crisis corridor systems, while maintaining a floor during crises with post-crisis bond sales is superior under pre-crisis floor systems. Both strategies enhance macroeconomic stability, inflation control, and welfare. The second essay addresses the qualitative dimension of monetary policy, particularly forward guidance, by proposing a novel identification strategy using sentiment analysis of news articles around Federal Open Market Committee (FOMC) meetings. Quantifying sentiment shifts related to interest rate guidance, balance sheet policies, and economic outlooks, it demonstrates that media-driven sentiment aligns with actual policy impacts, offering insights into expectation formation and financial market transmission. This approach mitigates endogeneity concerns in existing literature while disentangling forward guidance into its distinct components. The third essay investigates the underexplored link between U.S. unconventional monetary policy and commodity price surges post-pandemic. Combining vector error correction models (VECM), structural VARs (SVAR), and event studies, it shows that a 1 percentage point (pp) rise in the effective federal funds rate (EFFR) reduces commodity prices by 3.66 %, while a 1 pp cut in the proxy funds rate (PFR, representing unconventional policies) increases them by 47.65 %. Short-term effects are pronounced: contractionary EFFR shocks drive a 5 % decline, while expansionary unconventional shocks yield a 20 % rise within six months. Hawkish forward guidance sentiment further amplifies speculative behavior, underscoring unconventional policy's role in commodity market volatility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".