Bibliographic record
Abstract
I develop a model that improves upon the recent literature in open economy macroeconomics in that it lends itself more directly to empirical investigation. I solve the stationarity problem that characterizes many existing models by adopting an overlapping generations structure la Weil (1989). I model nominal rigidity by assuming that firms face explicit costs of output price inflation volatility. The specification generates an endogenous markup that fluctuates over the business cycle. I identify the two economies in my model with Canada--a small open economy--and the United States--taken as an approximation of the rest-of-the-world economy. In the second part of the paper, I present a plausible strategy for estimating the structural parameters of the Canadian economy. I do so by using nonlinear least squares at the single-equation level. Estimates of most parameters are characterized by small standard errors and are in line with the findings of other studies. I also develop a plausible way of constructing measures for nonobservable variables. To verify if multiple-equation regressions yield significantly different estimates, I run full information maximum likelihood, system-wide regressions. The results of the two procedures are similar. Finally, I illustrate a practical application of the model, showing how a shock to the U.S. economy is transmitted to Canada under an inflation-targeting monetary regime.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".