MétaCan
Menu
Back to cohort
Record W2271162147

The Rise of the Housing-Wealth Effect: Counterfactual Impulse Response Analysis

2014· article· en· W2271162147 on OpenAlexvenueno aff
Ryan R. Brady, Derek Stimel, Steven Sumner

Bibliographic record

VenueReview of Economics and Finance · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsWealth effectCounterfactual thinkingWealth elasticity of demandEconomicsNational wealthConsumption (sociology)Monetary economicsIndirect effectFinanceMonetary policy
DOInot available

Abstract

fetched live from OpenAlex

Abstract: This paper tests for the direct wealth effect versus an indirect wealth effect in aggregate data on U.S. households over four distinct sub-periods from 1952 through 2011. We use recent time series techniques to distinguish between the direct wealth effect from indirect channels which may operate through personal disposable income or liabilities. We find evidence of a direct wealth effect for housing wealth, in particular, from 1998 to 2011. The responses of consumption in the 1998 to 2011 period are in contrast to an indirect or “common cause ” explanation of the wealth effect. For financial wealth, there is some evidence of a direct wealth effect for the 1998 to 2011 period, but the effect overall is smaller than for tangible wealth. Also, before 1998 the evidence for a direct wealth effect from either housing wealth or financial wealth is weak.

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 imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueReview of Economics and FinanceSame topicHousing Market and EconomicsFrench-language works237,207