Microsimulating Residential Mobility and Spatial Search Behavior: Estimation of Continuous-Time Hazard and Discrete-Time Panel Logit Models for Residential Mobility
Bibliographic record
Abstract
This paper attempts to conceptualize the residential mobility and spatial search process to be implemented within a microsimulation-based integrated modeling system and presents empirical results of econometric models of mobility applying both discrete choice and hazard-based duration modeling techniques using Greater Toronto Area (GTA) retrospective survey data. It tests and compares fixed effects, random intercept and random parameter discrete-time panel logit models and parametric frailty models that account for unobserved heterogeneity. While the random parameter model (RP) performs better in identifying residential stressors that lead to mobility, the log-logistic Gaussian shared frailty model shows promising results in explaining termination of passive-state duration. The study reveals that most stressors that relate to life cycle events such as job change, birth of a child, increase/decrease in number of jobs etc. are significant in the RP Model. On the other hand, dwelling and neighborhood characteristics are dominant in the continuous-time shared frailty model.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".