MétaCan
Menu
Back to cohort
Record W2938015396

A Structural VAR Model for Estimating the Link between Monetary Policy and Home Prices in Israel

2017· preprint· en· W2938015396 on OpenAlexaboutno aff
Dana Orfaig

Bibliographic record

VenueRePEc: Research Papers in Economics · 2017
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMonetary policyEconomicsShock (circulatory)Quarter (Canadian coin)Monetary economicsVector autoregressionPoint (geometry)Interest rateMonetary baseGeography
DOInot available

Abstract

fetched live from OpenAlex

In recent years, the marked increase in home prices in Israel has prompted the need to understand the impact of monetary policy on home prices, including the mag- nitude and persistence of that impact. This paper finds that in response to a positive shock of 1 percentage point in the Bank of Israel's monetary interest rate, nominal home prices decline by 2.6 percent, and real home prices decline by 1.1 percent (and in a symmetrical manner to a negative shock). A broad international comparison indicates that the impact on home prices in Israel of a monetary shock is similar to the average impact worldwide. This paper adds to a wide global research base, and proposes-apparently for the first time in Israel-a structural VAR examination of the dynamic links between monetary policy and home prices. The VAR structure takes into account the main variables in the economy that affect, and are affected by, this link. The main conclusion is that monetary shocks, on their own, were not a dominant factor in explaining the changes in home prices in the research period-from the second quarter of 1995 through the first quarter of 2015.

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)0.003
metaresearch head score (Gemma)0.006
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.049
GPT teacher head0.320
Teacher spread0.271 · 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicIslamic Finance and Banking StudiesFrench-language works237,207