Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".