Bibliographic record
Abstract
This thesis examines the role of technological innovation in generating macroeconomic dynamics in open economies. Each chapter constitutes an original and independent contribution to the literature. In Chapter 1, I examine the behavioral responses of key macroeconomic variables in Canada to exogenous shocks to investment-specific technology. This is done by developing a stylized international real business cycle model which is simulated to explore its ability to shed new light on the dynamic behavior of the standard small open economy. The results indicate that this model can quantitatively replicate the key dynamic features of the post-war Canadian economy, and thus shocks to investment-specific technology can be considered an important exogenous process for studying and understanding modern macroeconomic dynamics in small open economies. In Chapter 2, I demonstrate that a two-country flexible price dynamic general equilibrium model driven by shocks to technology, and with a localized distribution services sector can replicate the key dynamic features of the real exchange rate. In doing so, the paper identifies the importance of two key channels for real exchange rate dynamics. In particular, I show: (i) that shocks in the real sector are important contributors to movements in the real exchange rate, and (ii) that the endogenous wedge created by the distribution costs of traded consumer goods is a significant source of fluctuation for the real exchange rate, and the overall macro-economy. Finally, in chapter 3 I analyze a dynamic general equilibrium model with a mixture of fiscal deficits, stochastic endowment, and sovereign debts. It offers an environment in which a loss of confidence in the sustainability of the government's fiscal position creates an environment for currency crises. The evidence provided demonstrates that the Argentine government's decision to abandon the peg in 2002---following the default on its international debts, was a self-fulfilling outcome of agent's expectations based on the underlying economic environment. Moreover, I also show that in an essentially identical economic framework---where the equilibrium probability of default is low, the optimal action for the government would be to maintain the fixed exchange rate and issue new debts to finance the fiscal deficit.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".