Bibliographic record
Abstract
In the first chapter “Impact of Quantitative Easing at the Zero Lower Bound (with J. Dorich, R. Mendes)”, we introduce imperfect asset substitution and segmented asset markets, along the lines of Andres et al. (2004), in an otherwise standard small open-economy model with nominal rigidities. We estimate the model using Canadian data. We use the model to provide a quantitative assessment of the macroeconomic impact of quantitative easing (QE) when the policy rate is at its effective lower bound. In the second chapter “Impact of Forward Guidance at the Zero Lower Bound”, I consider alternative monetary policy rules under commitment in a calibrated three-equation New Keynesian model and examine the extent to which forward guidance helps to mitigate the negative real impact of the zero lower bound. The simulation results suggest that the conditional statement policy prolongs the zero lower bound duration for an additional 4 quarters and reverses half of the decline in inflation associated with the lower bound. It even generates a period of overshooting in inflation three quarters after the initial negative demand shock. Alternatively, the effect of price-level targeting as a forward guidance policy at the zero lower bound is slightly different. In the third chapter “Impact of Quantitative Easing on Household Deleveraging”, I extend the DSGE model in the first chapter with some financial frictions to explore the effects of QE on asset prices and household balance sheet. There are two effects of QE on aggregate output originated from the model. First, QE leads to a decline in term premium, which increases current consumption relative to future consumption. Second, it leads to a lower loan to collateral value ratio and a decline in external finance premium. Favorable financing condition encourages further accumulation of household debt at cheaper rates, in turn, leads to an immediate higher household debt to income ratio. In the consideration of the future withdrawal of any stimulus provided from QE, this would pose greater challenges as it implies much intensive household deleveraging process. I provide some sensitivity analysis around key parameters of the model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".