Bibliographic record
Abstract
This study aims at making a short-term forecasting model in order to analyze short-term trend in Korean land market, using the land price fluctuation rate of nationwide data issued by the Ministry of Land, Infrastructure and Transport from the first quarter of 1987 to the first quarter of 2015. VECM model is used to predict the fluctuation rate of land from the first quarter of 2015 and the first quarter of 2017. The variables using the VECM model are nationwide average fluctuation rate in land prices as well as real GDP growth rate, CPI, yield of corporate bonds, rate of stock price rise and M3 in endogenous variables. As a result of Granger Sims Causality Test, using fluctuation rate of land price, real GDP growth rate, CPI, yield of corporate bonds, stock price, M3 permits for real GDP growth rate, yield of corporate bonds. Fluctuation rate of land price in 2015 is forecasted to be rised at 2.43% and 2.54% in 2016. Since land has distinct characteristics and economics situations such as locational stillness and various usabilities, the analysis results of land market nationwide does not actually lead to the accurate forecasting of land prices. Therefore, sub-markets characterized by regions and various usabilities need to be considered for researches. Any research on land market in connection with economics situations and government policies such as finances, taxes etc. will lead to more reliable forecasting results in the next researches.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".