Recent Developments in Empirical IO: Dynamic Demand and Dynamic Games
Bibliographic record
Abstract
Introduction Important aspects of competition in oligopoly markets are dynamic. Demand can be dynamic if products are storable or durable, or if utility from consumption is linked intertemporally. On the supply side, dynamics can be present as well. For example, investment and production decisions have dynamic implications if there is “learning-by-doing” or if there are sunk costs. Identifying the factors governing the dynamics is key to understanding competition and the evolution of market structure and for the evaluation of public policy. Advances in econometric methods and modeling techniques and the increased availability of data have led to a large body of empirical papers that study the dynamics of demand and competition in oligopoly markets. A key lesson learned early by most researchers is the complexity and challenges of modeling and estimating dynamic structural models. The complexity and “curse of dimensionality” are present even in relatively simple models but are especially problematic in oligopoly markets in which firms produce differentiated products or have heterogeneous costs. These sources of heterogeneity typically imply that the dimension of these models, and the computational cost of solving and estimating them, increases exponentially with the number of products and the number of firms. As a result, much of the recent work in structural econometrics in IO focuses on finding ways to make dynamic problems more tractable in terms of computation and careful modeling to reduce the state space while properly accounting for rich heterogeneity, dynamics, and strategic interactions.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".