An Analysis Of U.s. Demand For Certain Steel Products From Canada And Mexico
Bibliographic record
Abstract
This empirical study uses stochastic coefficients econometric modeling to forecast real exchange rate volatility and examine how expected and unexpected volatility affect bilateral trade flows of certain steel products between Canada, Mexico and the United States using monthly data for the seven-year period 1996-2002. The results of the model indicate that the effects of exchange rate volatility on bilateral trade flows for this sector are relatively minor, where sustained changes in the spot exchange rate, sectoral economic growth, and the price of goods being traded all exert more significant influence on trade levels than exchange rate volatility. However, the model results also tend to indicate that as exchange rate volatility increases, the well-developed U.S.-Canadian forward currency exchange market may present economic agents with profit opportunities through risk-portfolio diversification, resulting in a positive correlation between volatility and trade. For the less-developed U.S.-Mexican forward currency market, the model results indicate that the relationship between trade and volatility, both expected and unexpected, is weak and predominantly negative. iii DEDICATION To Allison, my love. iv ACKNOWLEDGMENTS I would like to take this opportunity to thank Aniene Porter, without whom I would not have entered the economics program, for her inexhaustible patience and unwavering support. Thanks very much to Dr. Nancy Lutz and Dr. Richard Ashley, without whom I would not have finished the program, for their invaluable time and guidance. Thanks also to my previous thesis committee members, especially Dr. Tom Lutton for his thoughtful comments throughout this process. Thanks to all the former Northern Virginia economics faculty members, especially Dr. Swamy for his...
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".