Agent Heterogeneity and the Real Exchange Rate
Bibliographic record
Abstract
While the impact of agent heterogeneity has long been recognized in the Economicliterature, the link between agent heterogeneity and international asset pricing is\nyet to be fully understood. In this dissertation I use an overlapping generations\nframework to study the impact that agent heterogeneity in risk aversion has on the\nreal exchange rate determination.\nChapter 1 presents and develops the theoretical model used to study the implications\nof agent heterogeneity in risk aversion on the real exchange rate. I introduce a twocountry\nmodel that features heterogeneous risk aversion profiles for agents, both\nwithin and between countries. Furthermore, it is shown that the model can explain\nthe Cyclicality puzzle documented in Backus and Smith (1993), which highlights the\nempirical disconnect between the exchange rate and relative consumption growth.\nThis chapter also presents the numerical outcome of the model.\nChapter 2 explains the quantitative methodology used to code and find the numerical\nsolution of the model presented in chapter 1. The model does not admit a closed\nform solution and thus the presented outcome relies on the application of Monte\nCarlo Methods, the Feynman-Kac Theorem and the Piccard Iteration Theorem.\nFinally, Chapter 3 presents recent empirical evidence on the Cyclicality puzzle between\nthe US and 4 OECD countries: UK, France, Germany and Italy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".