Study on Impact of House Prices on the Level of the Residents’ Consumption: Based on the Analysis of Liaoning Province
Bibliographic record
Abstract
Cointegration model and error correction model were developed based on the economic theory of consumption. The relation between urban consumption level and prices of commercial property were inspected in Liaoning Province. The study shows that the urban consumption level is promoted by the prices of commercial property in the long run and the short-term effect is the inhibition of consumption level due to “squeeze effect”. The government should safeguard the healthy development of estate market and promote the rapid stable growth of economic with Liaoning Province by the way of improving the individual disposable income and suppressing house price firmly. Key words: Cointegration; Error correction model; Squeeze effect Resume Le modele de la co-integration et le modele de correction d’erreur ont ete elabores sur la base de la theorie economique de la consommation. La relation entre le niveau de la consommation urbaine et les prix de l’immobilier commercial ont ete inspectes dans la province du Liaoning. L’etude montre que le niveau de consommation en milieu urbain est favorise parles prix de l’immobilier commercial a long terme et l’effet a court terme est l’inhibition de niveau de consommation en raison des «effects de pression». Le gouvernement devrait garantir le developpement sain du marche immobilier et d’y promouvoir la croissance rapide de la stabilite economique avec la province du Liaoning par le moyen d’amelioration du revenu individuel disponible et des prix des logements qui devraient etre supprimes fermement. Mots cles: Co-integration; Erreur de la correction du modele; Effect de la pression
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".