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Record W6927598497 · doi:10.34989/tr-18

Simulation Analysis of a Model Based on the Life-Cycle Hypothesis

2024· article· en· W6927598497 on OpenAlexaffabout

Bibliographic record

VenueBank of Canada Research · 2024
Typearticle
Languageen
FieldComputer Science
TopicLibrary Science and Information Systems
Canadian institutionsBank of Canada
Fundersnot available
KeywordsAggregate (composite)Representation (politics)Consumption (sociology)Econometric modelPopulationSet (abstract data type)Order (exchange)Yield (engineering)Aggregate data

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.690
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.097
GPT teacher head0.317
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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