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Cross-Country Evidence on the Causal Relationship between Policy Uncertainty and Housing Prices

2016· article· en· W46292476 on OpenAlexaboutno aff
Ghassen El Montasser, Ahdi Noomen Ajmi, Tsangyao Chang, Beatrice D. Simo‐Kengne, Christophe André, Rangan Gupta

Bibliographic record

VenueJournal of Housing Research · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsCausality (physics)Volatility (finance)Monetary policyPanel dataMacroeconomicsMonetary economicsEconometrics

Abstract

fetched live from OpenAlex

In this paper, we examine the causal linkages between policy uncertainty and housing prices in a panel of seven advanced countries including Canada, France, Germany, Italy, Spain, the United Kingdom, and the United States. We implement a bootstrap panel causality test on quarterly data from 2001:Q1 to 2013:Q1, which allows us to circumvent the data limitation as observations are pooled across countries. The results provide evidence of a bi-directional causality between real housing prices and policy uncertainty, suggesting that high uncertainty related to future economic fundamentals and policies increases housing price volatility, which in turn may amplify financial and business cycles. The results also show bi-directional causality for France and Spain, but only unidirectional causality for the remaining countries. Specifically, unidirectional causality runs from policy uncertainty to real housing prices in Canada, Germany and Italy and from real housing prices to policy uncertainty in the U.K. and the U.S.

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)0.009
metaresearch head score (Gemma)0.008
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 score0.084
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.256
GPT teacher head0.399
Teacher spread0.143 · 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

Citations71
Published2016
Admission routes1
Has abstractyes

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