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Record W2392010447

VECM 모형을 이용한 주택시장과 거시경제변수 관계 분석

2015· article· ko· W2392010447 on OpenAlexaboutno aff
김동환

Bibliographic record

Venue대한부동산학회지 · 2015
Typearticle
Languageko
FieldMedicine
TopicDiverse Approaches in Healthcare and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsEconometricsIndex (typography)Variance decomposition of forecast errorsMacroPrice indexProducer price indexQuarter (Canadian coin)Variance (accounting)Wholesale price indexPrice variancePrice levelMacroeconomicsMid price
DOInot available

Abstract

fetched live from OpenAlex

This research is the empirical study that macro-economic variables are set to impact on the relationship between the housing market and macro-economic variables by estimating the VECM model series, and it was analyzed the reaction for impact of macro-economic variables to house sales price index or house Jeonse price index using the impulse response function and variance decomposition based on the results of VECM model estimation from the first quarter, 1987 to the third quarter, 2015. The results of this study was that the affect relationship with house sales price index and house Jeonse price index for macro-economic variables had estimated VECM model by existing co-integration equations, and it appeared to be influenced in the direction of a positive direction(+) or a negative direction(-) for a unit impact of the standard deviation of macro economic variables to house sales price index and house Jense price index, also analyzed that the level may appear different in size according to each of the macro economic variables. As the results of variance decomposition, both of house sales price index and house Jeonse price index appeared to have been affected to undergo a significant impact by their own variations. Other micro-economic variables showed significantly affected by the bond but yield greater the degree of influence.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.009

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 head0.319
GPT teacher head0.409
Teacher spread0.089 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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