Bibliographic record
Abstract
This thesis consists of three essays in monetary economics and international macroeconomics. Chapter one uses Canadian data to evaluate the performance of money growth targeting and inflation targeting policy rules, especially when they react to asset price changes. There are three important findings. First, estimates of the policy rules consistent with both regimes provide evidence that the Bank of Canada has systematically reacted to stock price bubbles and exchange rate changes. Second, a counterfactual experiment reveals that, the high inflation of the 1970s and early 1980s could have been avoided if the Bank of Canada had responded more strongly to inflation and growth in aggregate demand. Third, simulation experiments yielded two important results: For both the money growth targeting and inflation targeting policy rules, it is always desirable to react to changes in exchange rates and stock price bubbles: Contrary to established findings, the results indicate that the money growth targeting policy rules are more efficient than the inflation targeting policy rules. Chapter two uses data on Ghana to test the validity of the intertemporal model of current account that allows for external shocks in the form of variable interest rates and exchange rates, and the existence of capital controls. We find that, irrespective of the degree of capital control, the basic model fails to predict the dynamics of the actual current account. However, we find that extending the model to capture variations in interest rates and exchange rates better explains the path of the actual current account balances only during the liberalized regime. When the model was adjusted to allow for credit constraints, there was some support for the proposition that the presence of capital controls prevented economic agents in Ghana to smooth their consumption path during the control regime. Chapter three investigates the effect of trading block on Tanzania's bilateral trade. Using a fixed effects estimation technique, the results revealed that the East African Community (EAC) and the European Union (EU) have had significant positive effects on Tanzania's bilateral trade. We also find that there is a significant intra-trade relationship between Tanzania and its major trading partners in the manufacturing sector.
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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 | 0.009 |
| 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 | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".