Spatial econometrics and the hedonic pricing model: what about the temporal dimension?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent ready access to free software and toolbox applications is directly impacting spatial econometric modelling when working with geolocated data. Spatial econometric models are valuable tools for taking into account the possible latent structure of the price determination process and ensuring that the coefficients estimated are unbiased and efficient. However, mechanical applications can potentially bias estimated coefficients if spatial data is pooled over time because the applications consider the spatial dimension alone. Spatial models neglect the fact that data (e.g. real estate) may consist of a collection of spatial data pooled over time, and that time relations generate a unidirectional effect as opposed to the multidirectional effect associated with spatial relations. Through an empirical case study, this paper addresses the possible bias in spatial autoregressive estimated parameters when data consist of spatial layers pooled over time. An empirical study is made using apartment sales in Paris between 1990 and 2001. Estimation results and out-of-sample predictions confirm, at least for this case, the hypothesis that ignoring the time dimension and applying spatial econometric tools generate divergence among the estimated autoregressive coefficients, which can potentially engender other serious problems.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it