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Record W3124309576

First Time Home Buyers and Residential Investment Volatility

2007· preprint· en· W3124309576 on OpenAlexfundno aff
Jonas D. M. Fisher, Martin Gervais

Bibliographic record

VenueRePEc: Research Papers in Economics · 2007
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsVolatility (finance)Great ModerationEconomicsMonetary economicsEarningsInvestment (military)Business cycleMacroeconomicsEconometricsFinance
DOInot available

Abstract

fetched live from OpenAlex

Like other macroeconomic variables, residential investment has become much less volatile since the mid-1980s (recent experience notwithstanding.) This paper explores the role of structural change in this decline. Since the early 1980s there have been many changes in the underlying structure of the economy, including those in the mortgage market which have made it easier to acquire a home. We examine how these changes affect residential investment volatility in a life-cycle model consistent with micro evidence on housing choices. We find that a decline in the rate of household formation, increased delay in marriage, and an increase in the cross-sectional variance of earnings drive the decline in volatility. Our findings provide support for the view that the ?Great Moderation? in aggregate fluctuations is not just due to smaller aggregate shocks, but is driven at least in part by structural change.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.036
GPT teacher head0.272
Teacher spread0.235 · 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 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

Citations12
Published2007
Admission routes1
Has abstractyes

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Same venueRePEc: Research Papers in EconomicsSame topicHousing Market and EconomicsFrench-language works237,207