Bibliographic record
Abstract
This dissertation consists of three essays in monetary economics. The first chapter studies the spillover effects of traditional US monetary policy to a number of advanced countries namely Canada, Denmark, the Eurozone, Japan, Switzerland and the United Kingdom. Using monthly data from January 1997 to December 2017, and a bivariate structural GARCH-in-Mean VAR, it finds that positive (negative) US monetary policy shocks increase (reduce) the policy rate in each of the other countries. It also finds that monetary policy uncertainty in the US has a negative and statistically significant effect on the monetary policy rate of each of the other countries. The second chapter investigates the effects of monetary policy shocks and uncertainty about monetary policy on key macroeconomic variables and interest rate spreads --- the term spread and credit spread. It uses monthly data for the US and a multivariate structural GARCH-in-Mean VAR model to estimate the effects on the growth rate of real output, the inflation rate, term spread, credit spread, and the policy rate. It finds statistically significant effects on all economic and financial variables. The final chapter studies international spillovers of conventional and new monetary policies of a large economy to a small open economy (SOE). It employs a medium-scale New Keynesian model that features all the major types of new monetary policies and the conventional monetary policy in a unified framework. In the quantitative application of the model, Canada is the SOE and the US is the large economy. The results show that there is little difference in the spillover effects of conventional and new monetary policies on the GDP of the SOE. However, the effects on various components of GDP (consumption, investment and net exports) differ by policy. Furthermore, it simulates counterfactual monetary policy scenarios for the US and Canada around the Great Recession of 2008. Three main conclusions emerge from these simulations: (1) If the Fed had not engaged in quantitative easing (QE), the US recession in the wake of 2008 financial crisis would have been deeper but Canada would have had better economic outcomes; (2) there are diminishing returns to QE in terms of its effects on both the US and Canadian real variables; and (3) had the Bank of Canada followed the Fed and engaged in QE of its own during the Great Recession, the real economic outcomes would have been better for 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".