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Record W6965132575 · doi:10.34989/tr-30

The Leading Indicator Properties of Surveyed Consumer Attitudes and Buying Intentions

2024· article· en· W6965132575 on OpenAlexaffabout

Bibliographic record

VenueBank of Canada Research · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsBank of Canada
Fundersnot available
KeywordsBivariate analysisIndex (typography)Consumer behaviourConsumer Expenditure SurveyMeasure (data warehouse)Survey data collectionConsumer price index (South Africa)

Abstract

fetched live from OpenAlex

The author attempts to discover whether the Conference Board of Canada's Survey of Consumer Buying Intentions is a useful leading indicator of consumer durable expenditure. Two aspects of the Survey are considered separately, the overall Index of Consumer Attitudes and the various buying intentions questions. Both are tested using bivariate reduced form Granger techniques and structural models of consumer behaviour. The results of the tests indicate that movements in the Index of Consumer Attitudes do tend to lead movements in durables expenditure by one quarter. This conclusion holds whether one uses the actual published Index, a variant including neutral responses, or a filtered version of the Index, where the filtered version is calculated as in a number of American studies. The relationship is especially significant for expenditures on automobiles and parts. There is little indication, however, that buying intentions as recorded in the Survey are useful leading indicators of subsequent consumer behaviour. Only in the furniture and floor coverings category is there a significant positive relationship between intentions and expenditure, and it is contemporaneous. One reason is that, while the Survey indicates what proportion of households intend to purchase certain items, it does not measure relative values of the intended purchases. Thus, a failure to link buying intentions with future expenditure is not an indictment of the accuracy or the validity of the Survey in its own terms.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.098
GPT teacher head0.292
Teacher spread0.193 · 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 designObservational
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

Citations1
Published2024
Admission routes2
Has abstractyes

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