A Simple General-Equilibrium Model of an International Economy
Bibliographic record
Abstract
In this chapter, we describe the basic framework that we use to undertake most of our analysis. In Section 3.1 we motivate our modeling assumptions and provide empirical support for these assumptions. In Section 3.2 we provide the details of the analytical model of a two-country endowment economy that we use throughout our analysis. In Section 3.3 we extend this model to allow for endogenous production decisions. Other extensions to the simple model that will be undertaken in the chapters to follow are described in Section 3.4. Motivation for the Modeling Assumptions Our objective is to understand the exchange rates between developed economies. We also wish to analyze the influence of financial markets on the flow of goods and financial capital between such economies, and through these flows, the effect on welfare and growth. Given these objectives, the framework we consider has the following characteristics: The model is of a dynamic, stochastic, general-equilibrium world economy in which decision rules for individual agents are derived from optimizing behavior. The model is one in which the economies of individual countries are distinct in that their commodity and financial markets need not be perfectly integrated. Monetary policy and the exchange regime matters, in the sense that they affect the allocation of real resources. We indicate below why these three features are important for a model used to address the issues considered in this monograph.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".