An Empirical Investigation of the Transmission and Effects of Monetary and Fiscal Policies
Bibliographic record
Abstract
Using statistical models, this dissertation investigates the transmission and effects of monetary and fiscal policies in the context of new challenges and environments that emerged following the 2008 global financial crisis. Chapter 2 analyzes an important step in the transmission of monetary policy---the interest rate pass-through from money market rates to consumer retail loan and deposit rates---in Canada from 1983 to 2015 using a nonlinear vector error-correction model. I find that pass-through was complete for all rates before the financial crisis but since the end of the 2008--09 recession, it has significantly declined for deposit rates. Chapter 3, co-authored with Margaux MacDonald, investigates the effects of unconventional monetary policy in Canada. We use recently proposed methods to construct a shadow interest rate that captures monetary policy at the zero lower bound and estimate a small open economy Bayesian structural vector autoregressive model. Controlling for the US macroeconomic and monetary policy variables, we find that Canadian unconventional monetary policy had expansionary effects on the Canadian economy. Chapter 4 shifts focus to fiscal policy. The rise in US partisan conflict following the Great Recession led to a popular belief that uncertainty about fiscal policy was impeding output growth. I explore this hypothesis by nesting it in a standard structural vector autoregression model traditionally used for estimating fiscal multipliers. I augment the model with stochastic volatility (a measure of uncertainty) and allow that to interact with the endogenous variables. I consider various trend assumptions, subsamples, information sets and estimation methods and find that the evidence does not support this hypothesis. The results reveal that there is no systematic relationship between fiscal policy uncertainty and output. Moreover, a time-varying parameter version of the model shows that the lack of consistency across specifications is not driven by changes in the transmission of uncertainty shocks over time.
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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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".