Bibliographic record
Abstract
Chapter 2 provides a tractable model that separates firms’ incentive problems and coordination problems during the initiation of collusion. In the Chilean pharmaceutical industry, firms collude through price leadership. Collusion gradually diffuses among markets: firms collusively raise prices in a couple of markets per week. We propose a model of price leadership under the dynamic pricing game framework to incorporate the coordination problems by allowing firms’ beliefs about competitors’ conduct to be biased towards a competitive equilibrium. As firms observe supracompetitive prices, they adaptively learn that competitors are willing to collude. We show that gradualism is explained by the heterogeneous market characteristics as well as firms’ learning to coordinate. Chapter 3 develops the likelihood-ratio based test of the null hypothesis of a m₀-component model against an alternative of (m₀+1)-component model in the normal mixture panel regression. I show that the normal mixture panel regression does not suffer from the Fisher Information matrix degeneracy under the reparameterization proposed in Kasahara and Shimotsu [89]. As a result, the likelihood ratio test statistic can be approximated by a local quadratic expansion of squares and products of the reparameterized parameters. Moreover, I obtain the data-driven penalty function via computational experiments to attend to the unbounded likelihood ratio. In addition, I apply the test to random coefficient Cobb-Douglas production function estimation following the framework of Gandhi et al. [63] and Kasahara and Shimotsu [90]. The empirical findings suggest evidence of heterogeneous production technology beyond the Hicks-neutral technology factor. Chapter 4 develops a modified expectation-maximization(EM) algorithm to incorporate unobserved heterogeneity for the dynamic discrete choice model that does not require the finite dependence property. Following the Euler Equation(EE) representation of dynamic discrete decision problems, we provide an alternative conditional choice probability (CCP) value function representation that relies only on the CCP of one action. We illustrate the computational gains with Monte Carlo simulations.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".