The Non-linear Ripple Effect of Housing Prices in Taiwan: A Smooth Transition Regressive Model
Bibliographic record
Abstract
Being different from past research of regional housing prices, this paper employs smooth transition regression model, derived in Teräsvirta (1998), to investigate ripple effects among four regional house prices in Taiwan. The aim of this paper is to test whether a smooth transition regression model, which is capable of capturing this non-linear behaviour, can show a better characterisation of regional housing prices than a linear model. This empirical analysis applies the four regional house prices of Taiwan, including the capital in Taiwan, Taipei City, and its suburban area, New Taipei City, and the other two mega cities of Taichung City and Kaohsiung City, from the first quarter of 1998 to the second quarter of 2011. Using the changing rate of housing price of Taipei City to be the threshold variable, the empirical results of the smooth transition regression model show that the ripple effect exists between housing prices of New Taipei City and Taipei City, while there is no ripple effect between housing prices of New Taipei City, Taichung City and Kaohsiung City. Besides, this paper has presented evidence of a non-linear relationship between housing prices of New Taipei City and Taipei City. When the changing rate of housing price in Taipei City is lower than 15.02, increasing housing price of Taipei City will make the hosing price of New Taipei City rise. Inversely, if it is higher than 15.02, increasing housing price of Taipei City will make the hosing price of New Taipei City decrease.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".