Bibliographic record
Abstract
We have developed a general-equilibrium, intertemporal framework of a stochastic world economy. In this framework, decision rules of individual agents with rational expectations are derived based on optimizing behavior, and the prices of goods and financial securities, the interest rate, and the exchange rate are determined endogenously. We have explicitly modeled the segmentation of commodity markets by introducing a cost for shipping goods across countries; thus, our model allows for deviations from the law of one price. We have also considered the effect of the opening international financial markets on welfare. We have used this framework to understand the behavior of the spot and forward exchange rates and international trade in goods and financial claims. We have also evaluated tariff policy, monetary policy, and the choice of the exchange rate regime in this setting. We characterize the spot exchange rate in a fairly general setting: without restricting the number of goods and countries, the utility functions, production processes, or the nature of frictions in international goods markets, but assuming that financial markets are complete and integrated, we show that the nominal exchange rate reflects cross-country differences in initial wealths, and marginal indirect utilities of nominal spending. More important, the expression that we get for the exchange rate is a nonlinear one, with changing coefficients. De Grauwe, Dewachter, and Embrechts (1993) report that the behavior of exchange rate returns is complex and nonlinear.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.130 | 0.039 |
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".