Bibliographic record
Abstract
This thesis contains three chapters exploring topics related to industrial organization and housing. The first chapter studies demand estimation of products with unobservable and varying availability. I show that unobservable product availabilities are recoverable from firms' profit maximization conditions. Searching for optimal assortment can be very time-consuming, because the total number of assortments increases exponentially with the number of products. I show firms only need to choose from a foldable menu, wherein the number of assortments equals the number of products. I provide an empirical application of the foldable menu model in China's tobacco industry. My results show that ignoring varying product availabilities leads to underestimated price elasticity (0.38 vs. 1.2) and thus false tax policy implications. Making all cigarettes available positively affects consumer welfare (+6.75%) and cigarette sales (+12.11%) but negatively affects wholesale profits (-5.95%).The second chapter proposes a penalized estimator for random coefficients demand models and tests its performance. Berry et al. (1995) proposed the widely used estimator for random coefficients demand models, the BLP estimator, which requires the inversion of model-predicted market shares using contraction mapping to obtain mean utilities and then uses the generalized method of moments (GMM) for estimation. The inversion step requires many iterations and thus is computationally costly. We propose a penalized estimator for random coefficients demand models, where the inversion step is unnecessary. We augment the GMM objective function with a penalty for the distance between observed market shares and predicted ones. Monte Carlo experiment results show that when using an appropriate starting point of search, the performance of our estimator is comparable with that of the BLP estimator. The third chapter empirically tests the effects of the Ontario Non-Resident Speculation Tax on the Greater Toronto Area's housing markets. Using house transaction data and the hedonic model, I find that the housing markets of areas preferred by domestic Chinese residents were more adversely affected by the Non-Resident Speculation Tax than those of the rest Greater Toronto Area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".