Simulation Analysis of a Model Based on the Life-Cycle Hypothesis
Bibliographic record
Abstract
This study was undertaken in order to provide better insight into the dynamics of the life-cycle model and to develop a set of quantitative relationships that model the behaviour of macroeconomic aggregates. To begin, a microeconomic model was constructed which could to a certain extent, reproduce behaviour patterns based on the life-cycle hypothesis and, with the aid of observed data on the Canadian population structure, generate a number of macroeconomic variables implied by this behaviour. The dynamics of this model were then examined in a number of simulations in which the effects of variations in population structure, as well as developments in incomes and the interest rate were analyzed. In a third step, we attempted to estimate certain standard consumption functions using the aggregate data generated by the model. While the estimates we obtained met current econometric criteria, these functions, especially where the interest rate was concerned, did not adequately reproduce the model used to generate the data. Finally we tested other formulations which seemed likely to yield a more acceptable representation of the basic model. This exercise, however, proved disappointing. In conclusion, it was ascertained that the study will have to be further refined in order to integrate into the macroeconomic formulations certain adjustments that would take into account demographic variations as well as the intertemporal substitution effect.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".